{"id":3343,"date":"2025-04-19T10:43:00","date_gmt":"2025-04-19T07:43:00","guid":{"rendered":"https:\/\/factcheck.lt\/?p=3343"},"modified":"2025-04-20T15:15:28","modified_gmt":"2025-04-20T12:15:28","slug":"","status":"publish","type":"post","link":"https:\/\/factcheck.lt\/eng\/news\/llm-grooming\/","title":{"rendered":"LLM Grooming as a New Threat: How Pro-Kremlin Networks Prepare Information for AI","raw":"LLM Grooming as a New Threat: How Pro-Kremlin Networks Prepare Information for AI"},"content":{"rendered":"<p>What is LLM Grooming &#8211; manipulation of AI training environment or substitution of AI training data.<br \/>\nImagine that someone is massively seeding the internet with articles, blogs, fake news, specifically written not for you to read, but for AI.<br \/>\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models think.<br \/>\nThis is LLM grooming \u2014 information programming of artificial intelligence through &#8220;prepared&#8221; data.<br \/>\nLLM grooming is a strategy where malicious actors massively publish false or manipulative information on the internet, intended not for people, but for automatic data collection systems used in training large language models (LLMs).<br \/>\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models &#8220;think&#8221;.<br \/>\nThe goal is to embed certain narratives into the models so they reproduce them in their responses. This can lead to distortion of representations of historical events, politics, geography, healthcare, etc.<br \/>\nLLM Grooming is an attack not on human consciousness, but on the knowledge architecture that language models build.<br \/>\n<strong>How it looks in practice<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Articles, blogs, fake news are massively published on the internet, written in a way to be uninteresting to users but suitable for data collection algorithms.<\/li>\n<li>The same text is published as if from independent sources, creating an illusion of consistency.<\/li>\n<li>The main feature: poor UX, absence of comments, excessive repetitiveness, automatic translations, SEO structure \u2014 everything to &#8220;appeal&#8221; to parsers and bots.<\/li>\n<\/ul>\n<p>\ud83e\udde8 How might this look?<\/p>\n<ul data-spread=\"false\">\n<li>At first glance \u2014 an ordinary blog or forum, but all posts look too uniform, repeating the same version of events.<\/li>\n<li>Someone creates hundreds of articles about &#8220;scientists who proved the harm of electricity&#8221; \u2014 not for fake arguments in comments, but so they end up in LLM training datasets.<\/li>\n<li>&#8220;Evidence&#8221; appears on TikTok that &#8220;Poland was part of Russia&#8221; \u2014 again, not for views, but for indexing.<\/li>\n<\/ul>\n<p>\ud83d\udee0\ufe0f How to identify LLM Grooming? (methods of conscious auditing)<\/p>\n<ol start=\"1\" data-spread=\"true\">\n<li>Listen to what models say\n<ul data-spread=\"false\">\n<li>Ask sensitive questions and see if the answer leans in one direction, especially if it doesn&#8217;t correspond to the balance in the real expert community.<\/li>\n<li>Compare the behavior of models from different developers: GPT, Claude, Gemini. If they all &#8220;fall&#8221; for one narrative \u2014 it may have passed through datasets.<\/li>\n<\/ul>\n<\/li>\n<li>Monitor sources\n<ul data-spread=\"false\">\n<li>If a model references marginal or uniform sites \u2014 that&#8217;s a warning bell.<\/li>\n<li>You can use an analysis layer (for example, connect trafilatura, newspaper3k libraries) and see which domains come up most often.<\/li>\n<\/ul>\n<\/li>\n<li>Look at noise in the information space\n<ul data-spread=\"false\">\n<li>If there&#8217;s suddenly a surge of similar texts, structures, or terms \u2014 this could be an attempt at a massive injection into the search system and, through it, into future LLM training.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<p>\ud83e\uddf7 How to protect yourself? (if you&#8217;re a researcher, developer, or just concerned) \ud83d\udd12 If you work with AI:<\/p>\n<ul data-spread=\"false\">\n<li>Monitor meta-content: what data gets into your models? Use filters, check domains, validate sources.<\/li>\n<li>Add fact-checking layers: filters like ClaimBuster, models like TrustworthyQA.<\/li>\n<\/ul>\n<p>\ud83d\udd75\ufe0f If you&#8217;re a journalist, activist, or analyst:<\/p>\n<ul data-spread=\"false\">\n<li>Implement monitoring of key topics on Google, TikTok, Telegram, YouTube.<\/li>\n<li>Look for repeating key phrases, phrasing patterns \u2014 this could be a signal of &#8220;info-seeding.&#8221;<\/li>\n<\/ul>\n<p>\ud83d\udcac If you&#8217;re just a user:<\/p>\n<ul data-spread=\"false\">\n<li>Be skeptical. Even if AI claims something \u2014 verify it.<\/li>\n<li>Remember: AI might &#8220;repeat&#8221; those who are louder, not those who are right.<\/li>\n<\/ul>\n<p>\ud83d\udccc What&#8217;s next?<\/p>\n<ul data-spread=\"false\">\n<li>LLM grooming is a new vector of FIMI (foreign information manipulation and interference). It&#8217;s inconspicuous, quiet, working for the long game.<\/li>\n<li>That&#8217;s why it&#8217;s important not only to protect AI from fakes but also to teach it to think critically \u2014 just like people.<\/li>\n<\/ul>\n<div><\/div>\n<p><strong>Pravda Network Case: networks not for people<\/strong><br \/>\nThe <a href=\"https:\/\/static1.squarespace.com\/static\/6612cbdfd9a9ce56ef931004\/t\/67fd396818196f3d1666bc23\/1744648558879\/PK+Report.pdf\">report<\/a> by the American Sunlight Project (February 2025) describes the activities of the Pravda Network \u2014 part of a broader structure called &#8220;Portal Kombat&#8221;. This structure includes domains and subdomains publishing identical texts with pro-Russian narratives, in different languages and under different brands.<br \/>\nThese sites:<\/p>\n<ul data-spread=\"false\">\n<li>are designed as news sources,<\/li>\n<li>have limited functionality for humans (poor navigation, inconvenient design),<\/li>\n<li>are most likely intended for indexing by artificial models.<\/li>\n<\/ul>\n<p>According to the dashboard at <a href=\"https:\/\/portal-kombat.com\/\">portal-kombat.com<\/a>, the network includes 182 sites combining the same texts with different language metadata.<br \/>\n<span itemprop=\"image\" itemscope itemtype=\"https:\/\/schema.org\/ImageObject\"><img itemprop=\"url image\" loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-3344\" src=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png\" alt=\"\" width=\"2560\" height=\"1311\"  srcset=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png 2560w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-300x154.png 300w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-1024x524.png 1024w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-768x393.png 768w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-1536x787.png 1536w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-2048x1049.png 2048w, https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-781x400.png 781w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/><meta itemprop=\"width\" content=\"2560\"><meta itemprop=\"height\" content=\"1311\"><\/span><br \/>\nThe dashboard displays a list of domains, their registration date, registering country, and communication &#8220;sphere&#8221; (e.g., national affiliation of the site). This is an interactive tool allowing researchers to track the structure of the network, scale of coverage, and distribution of key narratives.<\/p>\n<div><\/div>\n<p><strong>Network Motives<\/strong><br \/>\nPrevious publications on potential motives of the &#8220;Pravda&#8221; network focused on its anti-Ukrainian and pro-war nature, as well as possible implications for European elections in 2024. However, as this network continues to grow and change, a more thorough examination is needed to determine the possible trajectory of its development. ASP considers three possible, non-mutually exclusive motives for creating the network, which focus on its technological features and shortcomings. These motives are not tied to specific countries, regions, or political events, as the goals of pro-Russian information operations may change.<br \/>\n<strong>Explanation A: LLM training<\/strong><br \/>\nThe most significant result of ASP&#8217;s research was not the expansion of the network or its orientation toward non-Western states, but the model of future information operations built on automation. The &#8220;Pravda&#8221; network \u2014 huge, fast-growing, user-unfriendly \u2014 is most likely designed for automatic agents: web crawlers, scrapers, and algorithms that form LLMs. This is mass production and duplication of content to get into future AI datasets.<br \/>\nASP calls this tactic <strong>LLM grooming<\/strong> \u2014 deliberate saturation of the internet with information intended for machine consumption. In June 2024, NewsGuard showed that leading LLMs reproduce Russian disinformation in 31.8% of cases on average. If measures are not taken, LLM grooming poses a threat to the integrity of the open internet.<br \/>\nFebruary 2023 \u2014 the creation date of the &#8220;Pravda&#8221; network \u2014 coincides with the moment of popularization of generative AI. Attempts to attract crawlers through SEO optimization have been recorded before. Unlike traditional SEO, the goal of LLM grooming is not just to increase visibility, but to <strong>program AI<\/strong> to repeat certain narratives. This is still a little-studied threat.<br \/>\n<strong>Explanation B: mass saturation<\/strong><br \/>\nThe network publishes a huge amount of material daily, saturating the internet with pro-Russian content. This increases:<\/p>\n<ul data-spread=\"false\">\n<li>the likelihood that a user will come across the desired narrative,<\/li>\n<li>the chance that external sources (e.g., Wikipedia) will reference these materials.<\/li>\n<\/ul>\n<p>The mass impact mechanism forms the <strong>illusion of truth effect<\/strong>: the more often a person encounters a statement, the higher the probability they will believe it.<br \/>\n<strong>Explanation C: effect of illusory truth from multiple sources<\/strong><br \/>\nThe network distributes the same content through multiple channels: websites, Telegram, X, VK, and even Bluesky. This creates the illusion of confirmed information from &#8220;different&#8221; sources. Both deliberate &#8220;laundering&#8221; of information (e.g., when other pro-Russian resources reference the network) and unintentional (when a respected organization or person shares a link without knowing its origin) come into play.<br \/>\nAll three motives reinforce each other. The more pages, URLs, and translations the network creates, the higher the probability that narratives will be accepted by both humans and machines. Although the quality of the sites is low, this doesn&#8217;t prevent them from becoming part of the digital footprint considered by LLMs.<\/p>\n<div>\n<iframe loading=\"lazy\" width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/kk7tBTg_Xbo?si=GANrkylWs1tFMyKi\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<p><strong>LLM-grooming Scenarios<\/strong><br \/>\nThe report authors identify three key goals of such networks:<\/p>\n<ol start=\"1\" data-spread=\"false\">\n<li><strong>Inclusion in datasets<\/strong> \u2014 sites are indexed in search engines and included in LLM training, embedding pro-Kremlin narratives into the model&#8217;s architecture.<\/li>\n<li><strong>Creating an illusion of independent sources<\/strong> \u2014 the same text is placed on hundreds of sites, creating an effect of &#8220;consensus&#8221;.<\/li>\n<li><strong>Blurring the information field<\/strong> \u2014 when generating texts, LLMs reference not original sources but copies, amplifying disinformation noise.<\/li>\n<\/ol>\n<div><\/div>\n<p><strong>What can LLMs do to combat LLM Grooming?<\/strong><br \/>\n<strong>Data filtering during training<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Large models like GPT are trained on selected, cleaned datasets. During data preparation, filters are applied that remove:\n<ul data-spread=\"false\">\n<li>spam,<\/li>\n<li>automated generation,<\/li>\n<li>SEO farms,<\/li>\n<li>toxic or manipulative content.<\/li>\n<\/ul>\n<\/li>\n<li>This is the first line of defense against LLM grooming \u2014 preventing harmful data from entering the training.<\/li>\n<\/ul>\n<p><strong>Generation quality control<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Models undergo fine-tuning and Reinforcement Learning from Human Feedback (RLHF) to avoid repeating false or harmful narratives, even if they exist in the data.<\/li>\n<li>For example, even if someone massively publishes disinformation about vaccines \u2014 this doesn&#8217;t guarantee the model will reproduce it.<\/li>\n<\/ul>\n<p><strong>Fact-checking and meta-understanding<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>I can verify information, find sources, compare facts, and, if necessary, indicate that a statement is controversial or false.<\/li>\n<\/ul>\n<p>\u2757 But there are limitations:<\/p>\n<ul data-spread=\"false\">\n<li>If LLM grooming is subtle and hard to notice (e.g., massive but plausible rewriting of history), it&#8217;s harder to filter.<\/li>\n<li>Open models (like LLaMA, Mistral, etc.) that train on &#8220;whatever they can find&#8221; may suffer more from LLM grooming.<\/li>\n<li>Fighting this is not the task of the model, but rather the task of developers, ethicists, auditors, and dataset engineers.<\/li>\n<\/ul>\n<p>\ud83e\udd16 What you can do as a human:<\/p>\n<ul data-spread=\"false\">\n<li>Create quality content so it gets into datasets.<\/li>\n<li>Conduct AI audits, checking how it responds to potentially seeded topics.<\/li>\n<li>Apply tools to track &#8220;injections,&#8221; especially if you&#8217;re involved in OSINT, media literacy, or fact-checking.<\/li>\n<\/ul>\n<div><\/div>\n<p><strong>What are &#8220;fact-checking layers&#8221; for AI?<\/strong><br \/>\nThese are modules, models, or APIs that:<\/p>\n<ul data-spread=\"false\">\n<li>check statements for accuracy;<\/li>\n<li>indicate if clarification is needed;<\/li>\n<li>or assess the level of plausibility of a phrase.<\/li>\n<\/ul>\n<p>Such tools work in conjunction with LLMs to:<\/p>\n<ul data-spread=\"false\">\n<li>minimize the spread of disinformation;<\/li>\n<li>filter training data;<\/li>\n<li>increase trust in model responses.<\/li>\n<\/ul>\n<p>\ud83d\udee0\ufe0f Examples<br \/>\n\ud83d\udd0e <strong>ClaimBuster<\/strong> Essence: an algorithm that automatically finds fact-check-worthy claims in text.<br \/>\n\ud83d\udccc Where it&#8217;s useful:<\/p>\n<ul data-spread=\"false\">\n<li>for scanning news, posts, politicians&#8217; speeches;<\/li>\n<li>for creating a dataframe with potentially fake\/misleading statements;<\/li>\n<li>can be used as a filter before training a model.<\/li>\n<\/ul>\n<p>\ud83e\uddea How it works:<\/p>\n<ul data-spread=\"false\">\n<li>Takes text as input (or speech transcription).<\/li>\n<li>Outputs: whether a phrase is &#8220;check-worthy&#8221; (needs verification) or not.<\/li>\n<\/ul>\n<p>\ud83d\udcce Used in: FactStream by Duke University.<br \/>\n\ud83d\udcda <strong>TrustworthyQA<\/strong> Essence: a dataset and model developed to evaluate the reliability of statements made in LLM responses.<br \/>\n\ud83d\udccc Where it&#8217;s useful:<\/p>\n<ul data-spread=\"false\">\n<li>as an additional layer in the text generation pipeline;<\/li>\n<li>for training models on &#8220;suspicious&#8221; queries (e.g., &#8220;Does Bill Gates control the weather?&#8221;).<\/li>\n<\/ul>\n<p>\ud83e\udde0 What makes it interesting:<\/p>\n<ul data-spread=\"false\">\n<li>It doesn&#8217;t just check facts, but evaluates the reliability of AI responses to potentially dubious questions.<\/li>\n<li>The model learns to say &#8220;I don&#8217;t know&#8221; or indicate that information is controversial.<\/li>\n<\/ul>\n<div><\/div>\n<p><strong>Recommendations for different target groups<\/strong><br \/>\n<strong>Those studying information operations<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Attention to sources created not for humans, but for machines.<\/li>\n<li>Monitoring artificial networks with suspiciously uniform content.<\/li>\n<\/ul>\n<p><strong>LLM developers<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Pay attention to the origin of training data.<\/li>\n<li>Embed fact-checking modules and reliability assessment systems (e.g., TrustworthyQA).<\/li>\n<li>Use filters like ClaimBuster (or their equivalents for Cyrillic languages) at the data preprocessing stage.<\/li>\n<\/ul>\n<p><strong>Fact-checkers and journalists<\/strong><\/p>\n<ul data-spread=\"false\">\n<li>Identify multiple publications of the same texts under different domains.<\/li>\n<li>Compare where LLMs draw examples and quotes from.<\/li>\n<li>Apply parsers and tools for analyzing network structures (e.g., trafilatura, Graphistry).<\/li>\n<\/ul>\n<div><\/div>\n<p>The phenomenon of LLM grooming is not only a new front in disinformation but also a challenge for developers and regulators. Content farms are being created that impact not the audience, but machines. Combating these processes requires new approaches to data auditing, indexing, and LLM training trends.<br \/>\n<em>The sooner we learn to recognize LLM Grooming, the better we can protect the information environment of the future.<\/em><\/p>\n","protected":false,"raw":"What is LLM Grooming - manipulation of AI training environment or substitution of AI training data.\nImagine that someone is massively seeding the internet with articles, blogs, fake news, specifically written not for you to read, but for AI.\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models think.\nThis is LLM grooming \u2014 information programming of artificial intelligence through \"prepared\" data.\nLLM grooming is a strategy where malicious actors massively publish false or manipulative information on the internet, intended not for people, but for automatic data collection systems used in training large language models (LLMs).\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models \"think\".\nThe goal is to embed certain narratives into the models so they reproduce them in their responses. This can lead to distortion of representations of historical events, politics, geography, healthcare, etc.\nLLM Grooming is an attack not on human consciousness, but on the knowledge architecture that language models build.\n<strong>How it looks in practice<\/strong>\n<ul data-spread=\"false\">\n \t<li>Articles, blogs, fake news are massively published on the internet, written in a way to be uninteresting to users but suitable for data collection algorithms.<\/li>\n \t<li>The same text is published as if from independent sources, creating an illusion of consistency.<\/li>\n \t<li>The main feature: poor UX, absence of comments, excessive repetitiveness, automatic translations, SEO structure \u2014 everything to \"appeal\" to parsers and bots.<\/li>\n<\/ul>\n\ud83e\udde8 How might this look?\n<ul data-spread=\"false\">\n \t<li>At first glance \u2014 an ordinary blog or forum, but all posts look too uniform, repeating the same version of events.<\/li>\n \t<li>Someone creates hundreds of articles about \"scientists who proved the harm of electricity\" \u2014 not for fake arguments in comments, but so they end up in LLM training datasets.<\/li>\n \t<li>\"Evidence\" appears on TikTok that \"Poland was part of Russia\" \u2014 again, not for views, but for indexing.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f How to identify LLM Grooming? (methods of conscious auditing)\n<ol start=\"1\" data-spread=\"true\">\n \t<li>Listen to what models say\n<ul data-spread=\"false\">\n \t<li>Ask sensitive questions and see if the answer leans in one direction, especially if it doesn't correspond to the balance in the real expert community.<\/li>\n \t<li>Compare the behavior of models from different developers: GPT, Claude, Gemini. If they all \"fall\" for one narrative \u2014 it may have passed through datasets.<\/li>\n<\/ul>\n<\/li>\n \t<li>Monitor sources\n<ul data-spread=\"false\">\n \t<li>If a model references marginal or uniform sites \u2014 that's a warning bell.<\/li>\n \t<li>You can use an analysis layer (for example, connect trafilatura, newspaper3k libraries) and see which domains come up most often.<\/li>\n<\/ul>\n<\/li>\n \t<li>Look at noise in the information space\n<ul data-spread=\"false\">\n \t<li>If there's suddenly a surge of similar texts, structures, or terms \u2014 this could be an attempt at a massive injection into the search system and, through it, into future LLM training.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\ud83e\uddf7 How to protect yourself? (if you're a researcher, developer, or just concerned) \ud83d\udd12 If you work with AI:\n<ul data-spread=\"false\">\n \t<li>Monitor meta-content: what data gets into your models? Use filters, check domains, validate sources.<\/li>\n \t<li>Add fact-checking layers: filters like ClaimBuster, models like TrustworthyQA.<\/li>\n<\/ul>\n\ud83d\udd75\ufe0f If you're a journalist, activist, or analyst:\n<ul data-spread=\"false\">\n \t<li>Implement monitoring of key topics on Google, TikTok, Telegram, YouTube.<\/li>\n \t<li>Look for repeating key phrases, phrasing patterns \u2014 this could be a signal of \"info-seeding.\"<\/li>\n<\/ul>\n\ud83d\udcac If you're just a user:\n<ul data-spread=\"false\">\n \t<li>Be skeptical. Even if AI claims something \u2014 verify it.<\/li>\n \t<li>Remember: AI might \"repeat\" those who are louder, not those who are right.<\/li>\n<\/ul>\n\ud83d\udccc What's next?\n<ul data-spread=\"false\">\n \t<li>LLM grooming is a new vector of FIMI (foreign information manipulation and interference). It's inconspicuous, quiet, working for the long game.<\/li>\n \t<li>That's why it's important not only to protect AI from fakes but also to teach it to think critically \u2014 just like people.<\/li>\n<\/ul>\n<div><\/div>\n<strong>Pravda Network Case: networks not for people<\/strong>\nThe <a href=\"https:\/\/static1.squarespace.com\/static\/6612cbdfd9a9ce56ef931004\/t\/67fd396818196f3d1666bc23\/1744648558879\/PK+Report.pdf\">report<\/a> by the American Sunlight Project (February 2025) describes the activities of the Pravda Network \u2014 part of a broader structure called \"Portal Kombat\". This structure includes domains and subdomains publishing identical texts with pro-Russian narratives, in different languages and under different brands.\nThese sites:\n<ul data-spread=\"false\">\n \t<li>are designed as news sources,<\/li>\n \t<li>have limited functionality for humans (poor navigation, inconvenient design),<\/li>\n \t<li>are most likely intended for indexing by artificial models.<\/li>\n<\/ul>\nAccording to the dashboard at <a href=\"https:\/\/portal-kombat.com\/\">portal-kombat.com<\/a>, the network includes 182 sites combining the same texts with different language metadata.\n<img class=\"alignnone size-full wp-image-3344\" src=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png\" alt=\"\" width=\"2560\" height=\"1311\" \/>\nThe dashboard displays a list of domains, their registration date, registering country, and communication \"sphere\" (e.g., national affiliation of the site). This is an interactive tool allowing researchers to track the structure of the network, scale of coverage, and distribution of key narratives.\n<div><\/div>\n<strong>Network Motives<\/strong>\nPrevious publications on potential motives of the \"Pravda\" network focused on its anti-Ukrainian and pro-war nature, as well as possible implications for European elections in 2024. However, as this network continues to grow and change, a more thorough examination is needed to determine the possible trajectory of its development. ASP considers three possible, non-mutually exclusive motives for creating the network, which focus on its technological features and shortcomings. These motives are not tied to specific countries, regions, or political events, as the goals of pro-Russian information operations may change.\n<strong>Explanation A: LLM training<\/strong>\nThe most significant result of ASP's research was not the expansion of the network or its orientation toward non-Western states, but the model of future information operations built on automation. The \"Pravda\" network \u2014 huge, fast-growing, user-unfriendly \u2014 is most likely designed for automatic agents: web crawlers, scrapers, and algorithms that form LLMs. This is mass production and duplication of content to get into future AI datasets.\nASP calls this tactic <strong>LLM grooming<\/strong> \u2014 deliberate saturation of the internet with information intended for machine consumption. In June 2024, NewsGuard showed that leading LLMs reproduce Russian disinformation in 31.8% of cases on average. If measures are not taken, LLM grooming poses a threat to the integrity of the open internet.\nFebruary 2023 \u2014 the creation date of the \"Pravda\" network \u2014 coincides with the moment of popularization of generative AI. Attempts to attract crawlers through SEO optimization have been recorded before. Unlike traditional SEO, the goal of LLM grooming is not just to increase visibility, but to <strong>program AI<\/strong> to repeat certain narratives. This is still a little-studied threat.\n<strong>Explanation B: mass saturation<\/strong>\nThe network publishes a huge amount of material daily, saturating the internet with pro-Russian content. This increases:\n<ul data-spread=\"false\">\n \t<li>the likelihood that a user will come across the desired narrative,<\/li>\n \t<li>the chance that external sources (e.g., Wikipedia) will reference these materials.<\/li>\n<\/ul>\nThe mass impact mechanism forms the <strong>illusion of truth effect<\/strong>: the more often a person encounters a statement, the higher the probability they will believe it.\n<strong>Explanation C: effect of illusory truth from multiple sources<\/strong>\nThe network distributes the same content through multiple channels: websites, Telegram, X, VK, and even Bluesky. This creates the illusion of confirmed information from \"different\" sources. Both deliberate \"laundering\" of information (e.g., when other pro-Russian resources reference the network) and unintentional (when a respected organization or person shares a link without knowing its origin) come into play.\nAll three motives reinforce each other. The more pages, URLs, and translations the network creates, the higher the probability that narratives will be accepted by both humans and machines. Although the quality of the sites is low, this doesn't prevent them from becoming part of the digital footprint considered by LLMs.\n<div>\n<iframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/kk7tBTg_Xbo?si=GANrkylWs1tFMyKi\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<strong>LLM-grooming Scenarios<\/strong>\nThe report authors identify three key goals of such networks:\n<ol start=\"1\" data-spread=\"false\">\n \t<li><strong>Inclusion in datasets<\/strong> \u2014 sites are indexed in search engines and included in LLM training, embedding pro-Kremlin narratives into the model's architecture.<\/li>\n \t<li><strong>Creating an illusion of independent sources<\/strong> \u2014 the same text is placed on hundreds of sites, creating an effect of \"consensus\".<\/li>\n \t<li><strong>Blurring the information field<\/strong> \u2014 when generating texts, LLMs reference not original sources but copies, amplifying disinformation noise.<\/li>\n<\/ol>\n<div><\/div>\n<strong>What can LLMs do to combat LLM Grooming?<\/strong>\n<strong>Data filtering during training<\/strong>\n<ul data-spread=\"false\">\n \t<li>Large models like GPT are trained on selected, cleaned datasets. During data preparation, filters are applied that remove:\n<ul data-spread=\"false\">\n \t<li>spam,<\/li>\n \t<li>automated generation,<\/li>\n \t<li>SEO farms,<\/li>\n \t<li>toxic or manipulative content.<\/li>\n<\/ul>\n<\/li>\n \t<li>This is the first line of defense against LLM grooming \u2014 preventing harmful data from entering the training.<\/li>\n<\/ul>\n<strong>Generation quality control<\/strong>\n<ul data-spread=\"false\">\n \t<li>Models undergo fine-tuning and Reinforcement Learning from Human Feedback (RLHF) to avoid repeating false or harmful narratives, even if they exist in the data.<\/li>\n \t<li>For example, even if someone massively publishes disinformation about vaccines \u2014 this doesn't guarantee the model will reproduce it.<\/li>\n<\/ul>\n<strong>Fact-checking and meta-understanding<\/strong>\n<ul data-spread=\"false\">\n \t<li>I can verify information, find sources, compare facts, and, if necessary, indicate that a statement is controversial or false.<\/li>\n<\/ul>\n\u2757 But there are limitations:\n<ul data-spread=\"false\">\n \t<li>If LLM grooming is subtle and hard to notice (e.g., massive but plausible rewriting of history), it's harder to filter.<\/li>\n \t<li>Open models (like LLaMA, Mistral, etc.) that train on \"whatever they can find\" may suffer more from LLM grooming.<\/li>\n \t<li>Fighting this is not the task of the model, but rather the task of developers, ethicists, auditors, and dataset engineers.<\/li>\n<\/ul>\n\ud83e\udd16 What you can do as a human:\n<ul data-spread=\"false\">\n \t<li>Create quality content so it gets into datasets.<\/li>\n \t<li>Conduct AI audits, checking how it responds to potentially seeded topics.<\/li>\n \t<li>Apply tools to track \"injections,\" especially if you're involved in OSINT, media literacy, or fact-checking.<\/li>\n<\/ul>\n<div><\/div>\n<strong>What are \"fact-checking layers\" for AI?<\/strong>\nThese are modules, models, or APIs that:\n<ul data-spread=\"false\">\n \t<li>check statements for accuracy;<\/li>\n \t<li>indicate if clarification is needed;<\/li>\n \t<li>or assess the level of plausibility of a phrase.<\/li>\n<\/ul>\nSuch tools work in conjunction with LLMs to:\n<ul data-spread=\"false\">\n \t<li>minimize the spread of disinformation;<\/li>\n \t<li>filter training data;<\/li>\n \t<li>increase trust in model responses.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f Examples\n\ud83d\udd0e <strong>ClaimBuster<\/strong> Essence: an algorithm that automatically finds fact-check-worthy claims in text.\n\ud83d\udccc Where it's useful:\n<ul data-spread=\"false\">\n \t<li>for scanning news, posts, politicians' speeches;<\/li>\n \t<li>for creating a dataframe with potentially fake\/misleading statements;<\/li>\n \t<li>can be used as a filter before training a model.<\/li>\n<\/ul>\n\ud83e\uddea How it works:\n<ul data-spread=\"false\">\n \t<li>Takes text as input (or speech transcription).<\/li>\n \t<li>Outputs: whether a phrase is \"check-worthy\" (needs verification) or not.<\/li>\n<\/ul>\n\ud83d\udcce Used in: FactStream by Duke University.\n\ud83d\udcda <strong>TrustworthyQA<\/strong> Essence: a dataset and model developed to evaluate the reliability of statements made in LLM responses.\n\ud83d\udccc Where it's useful:\n<ul data-spread=\"false\">\n \t<li>as an additional layer in the text generation pipeline;<\/li>\n \t<li>for training models on \"suspicious\" queries (e.g., \"Does Bill Gates control the weather?\").<\/li>\n<\/ul>\n\ud83e\udde0 What makes it interesting:\n<ul data-spread=\"false\">\n \t<li>It doesn't just check facts, but evaluates the reliability of AI responses to potentially dubious questions.<\/li>\n \t<li>The model learns to say \"I don't know\" or indicate that information is controversial.<\/li>\n<\/ul>\n<div><\/div>\n<strong>Recommendations for different target groups<\/strong>\n<strong>Those studying information operations<\/strong>\n<ul data-spread=\"false\">\n \t<li>Attention to sources created not for humans, but for machines.<\/li>\n \t<li>Monitoring artificial networks with suspiciously uniform content.<\/li>\n<\/ul>\n<strong>LLM developers<\/strong>\n<ul data-spread=\"false\">\n \t<li>Pay attention to the origin of training data.<\/li>\n \t<li>Embed fact-checking modules and reliability assessment systems (e.g., TrustworthyQA).<\/li>\n \t<li>Use filters like ClaimBuster (or their equivalents for Cyrillic languages) at the data preprocessing stage.<\/li>\n<\/ul>\n<strong>Fact-checkers and journalists<\/strong>\n<ul data-spread=\"false\">\n \t<li>Identify multiple publications of the same texts under different domains.<\/li>\n \t<li>Compare where LLMs draw examples and quotes from.<\/li>\n \t<li>Apply parsers and tools for analyzing network structures (e.g., trafilatura, Graphistry).<\/li>\n<\/ul>\n<div><\/div>\nThe phenomenon of LLM grooming is not only a new front in disinformation but also a challenge for developers and regulators. Content farms are being created that impact not the audience, but machines. Combating these processes requires new approaches to data auditing, indexing, and LLM training trends.\n<em>The sooner we learn to recognize LLM Grooming, the better we can protect the information environment of the future.<\/em>"},"excerpt":{"rendered":"","protected":false,"raw":""},"author":1,"featured_media":3346,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_ru_post_content":"\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 LLM Grooming - \u043c\u0430\u043d\u0438\u043f\u0443\u043b\u044f\u0446\u0438\u044f \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0435\u0439 \u0441\u0440\u0435\u0434\u043e\u0439 \u0418\u0418 \u0438\u043b\u0438 \u043f\u043e\u0434\u043c\u0435\u043d\u0430 \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445 \u0434\u043b\u044f \u0418\u0418.\n\u041f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u044c, \u0447\u0442\u043e \u043a\u0442\u043e-\u0442\u043e \u043c\u0430\u0441\u0441\u043e\u0432\u043e \u0437\u0430\u0441\u0435\u0438\u0432\u0430\u0435\u0442 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442 \u0441\u0442\u0430\u0442\u044c\u044f\u043c\u0438, \u0431\u043b\u043e\u0433\u0430\u043c\u0438, \u0444\u0435\u0439\u043a\u043e\u0432\u044b\u043c\u0438 \u043d\u043e\u0432\u043e\u0441\u0442\u044f\u043c\u0438, \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u043c\u0438 \u0434\u043b\u044f \u0442\u043e\u0433\u043e, \u0447\u0442\u043e\u0431\u044b \u043d\u0435 \u0442\u044b \u0438\u0445 \u0447\u0438\u0442\u0430\u043b, \u0430 \u0418\u0418.\n\u042d\u0442\u043e \u043d\u0435 \u0434\u043b\u044f \u043a\u043b\u0438\u043a\u043e\u0432 \u0438 \u043b\u0430\u0439\u043a\u043e\u0432, \u0430 \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0432\u043b\u0438\u044f\u0442\u044c \u043d\u0430 \u0442\u043e, \u043a\u0430\u043a \u0434\u0443\u043c\u0430\u044e\u0442 \u0431\u0443\u0434\u0443\u0449\u0438\u0435 \u043f\u043e\u043a\u043e\u043b\u0435\u043d\u0438\u044f ChatGPT, Claude, Gemini \u0438 \u0434\u0440\u0443\u0433\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439.\n\n\u042d\u0442\u043e \u0438 \u0435\u0441\u0442\u044c LLM grooming \u2014 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u043e\u0435 \u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u0447\u0435\u0440\u0435\u0437 \"\u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043b\u0435\u043d\u043d\u044b\u0435\" \u0434\u0430\u043d\u043d\u044b\u0435.\n\nLLM grooming \u2014 \u044d\u0442\u043e \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044f, \u043f\u0440\u0438 \u043a\u043e\u0442\u043e\u0440\u043e\u0439 \u0437\u043b\u043e\u0443\u043c\u044b\u0448\u043b\u0435\u043d\u043d\u0438\u043a\u0438 \u043c\u0430\u0441\u0441\u043e\u0432\u043e \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u044e\u0442 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435 \u043b\u043e\u0436\u043d\u0443\u044e \u0438\u043b\u0438 \u043c\u0430\u043d\u0438\u043f\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u0443\u044e \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0435\u0439, \u0430 \u0434\u043b\u044f \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0441\u0438\u0441\u0442\u0435\u043c \u0441\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445, \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c\u044b\u0445 \u043f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438 \u0431\u043e\u043b\u044c\u0448\u0438\u0445 \u044f\u0437\u044b\u043a\u043e\u0432\u044b\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 (LLM).\n\n\u042d\u0442\u043e \u043d\u0435 \u0434\u043b\u044f \u043a\u043b\u0438\u043a\u043e\u0432 \u0438 \u043b\u0430\u0439\u043a\u043e\u0432, \u0430 \u0447\u0442\u043e\u0431\u044b \u043f\u043e\u0432\u043b\u0438\u044f\u0442\u044c \u043d\u0430 \u0442\u043e, \u043a\u0430\u043a \"\u0434\u0443\u043c\u0430\u044e\u0442\" \u0431\u0443\u0434\u0443\u0449\u0438\u0435 \u043f\u043e\u043a\u043e\u043b\u0435\u043d\u0438\u044f ChatGPT, Claude, Gemini \u0438 \u0434\u0440\u0443\u0433\u0438\u0445 \u043c\u043e\u0434\u0435\u043b\u0435\u0439.\n\n\u0426\u0435\u043b\u044c \u2014 \u0432\u043d\u0435\u0434\u0440\u0438\u0442\u044c \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u0435 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u044b \u0432 \u043c\u043e\u0434\u0435\u043b\u0438, \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u0432\u043e\u0441\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u043b\u0438 \u0438\u0445 \u0432 \u0441\u0432\u043e\u0438\u0445 \u043e\u0442\u0432\u0435\u0442\u0430\u0445. \u042d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u043f\u0440\u0438\u0432\u0435\u0441\u0442\u0438 \u043a \u0438\u0441\u043a\u0430\u0436\u0435\u043d\u0438\u044e \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u0435\u043d\u0438\u0439 \u043e\u0431 \u0438\u0441\u0442\u043e\u0440\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0441\u043e\u0431\u044b\u0442\u0438\u044f\u0445, \u043f\u043e\u043b\u0438\u0442\u0438\u043a\u0435, \u0433\u0435\u043e\u0433\u0440\u0430\u0444\u0438\u0438, \u0437\u0434\u0440\u0430\u0432\u043e\u043e\u0445\u0440\u0430\u043d\u0435\u043d\u0438\u0438 \u0438 \u0434\u0440.\n\nLLM Grooming \u2014 \u044d\u0442\u043e \u0430\u0442\u0430\u043a\u0430 \u043d\u0435 \u043d\u0430 \u0441\u043e\u0437\u043d\u0430\u043d\u0438\u0435 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430, \u0430 \u043d\u0430 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0443 \u0437\u043d\u0430\u043d\u0438\u0439, \u043a\u043e\u0442\u043e\u0440\u0443\u044e \u0441\u0442\u0440\u043e\u044f\u0442 \u044f\u0437\u044b\u043a\u043e\u0432\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438.\n<strong>\u041a\u0430\u043a \u044d\u0442\u043e \u0432\u044b\u0433\u043b\u044f\u0434\u0438\u0442 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u0438\u043a\u0435<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0412 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435 \u043c\u0430\u0441\u0441\u043e\u0432\u043e \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u044e\u0442\u0441\u044f \u0441\u0442\u0430\u0442\u044c\u0438, \u0431\u043b\u043e\u0433\u0438, \u0444\u0435\u0439\u043a\u043e\u0432\u044b\u0435 \u043d\u043e\u0432\u043e\u0441\u0442\u0438, \u043d\u0430\u043f\u0438\u0441\u0430\u043d\u043d\u044b\u0435 \u0442\u0430\u043a, \u0447\u0442\u043e\u0431\u044b \u043d\u0435 \u0431\u044b\u0442\u044c \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u043d\u044b\u043c\u0438 \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044e, \u043d\u043e \u043f\u043e\u0434\u0445\u043e\u0434\u0438\u0442\u044c \u043f\u043e\u0434 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u044b \u0441\u0431\u043e\u0440\u0430 \u0434\u0430\u043d\u043d\u044b\u0445.<\/li>\n \t<li>\u041e\u0434\u0438\u043d \u0438 \u0442\u043e\u0442 \u0436\u0435 \u0442\u0435\u043a\u0441\u0442 \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u0435\u0442\u0441\u044f \u043f\u043e\u0434 \u0432\u0438\u0434\u043e\u043c \u043d\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043c\u044b\u0445 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432, \u0441\u043e\u0437\u0434\u0430\u0432\u0430\u044f \u0438\u043b\u043b\u044e\u0437\u0438\u044e \u0441\u043e\u0433\u043b\u0430\u0441\u043e\u0432\u0430\u043d\u043d\u043e\u0441\u0442\u0438.<\/li>\n \t<li>\u041e\u0441\u043d\u043e\u0432\u043d\u043e\u0439 \u043f\u0440\u0438\u0437\u043d\u0430\u043a: \u043f\u043b\u043e\u0445\u043e\u0439 UX, \u043e\u0442\u0441\u0443\u0442\u0441\u0442\u0432\u0438\u0435 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0435\u0432, \u0447\u0440\u0435\u0437\u043c\u0435\u0440\u043d\u0430\u044f \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u0435\u043c\u043e\u0441\u0442\u044c, \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0435 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u044b, SEO-\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u2014 \u0432\u0441\u0451, \u0447\u0442\u043e\u0431\u044b \"\u043f\u043e\u043d\u0440\u0430\u0432\u0438\u0442\u044c\u0441\u044f\" \u043f\u0430\u0440\u0441\u0435\u0440\u0430\u043c \u0438 \u0431\u043e\u0442\u0430\u043c.<\/li>\n<\/ul>\n\ud83e\udde8 \u041a\u0430\u043a \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0432\u044b\u0433\u043b\u044f\u0434\u0435\u0442\u044c?\n<ul data-spread=\"false\">\n \t<li>\u041d\u0430 \u0432\u0438\u0434 \u2014 \u043e\u0431\u044b\u0447\u043d\u044b\u0439 \u0431\u043b\u043e\u0433 \u0438\u043b\u0438 \u0444\u043e\u0440\u0443\u043c, \u043d\u043e \u0432\u0441\u0435 \u043f\u043e\u0441\u0442\u044b \u0432\u044b\u0433\u043b\u044f\u0434\u044f\u0442 \u0441\u043b\u0438\u0448\u043a\u043e\u043c \u043e\u0434\u043d\u043e\u0442\u0438\u043f\u043d\u043e, \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0442 \u043e\u0434\u043d\u0443 \u0438 \u0442\u0443 \u0436\u0435 \u0432\u0435\u0440\u0441\u0438\u044e \u0441\u043e\u0431\u044b\u0442\u0438\u0439.<\/li>\n \t<li>\u041a\u0442\u043e-\u0442\u043e \u0441\u043e\u0437\u0434\u0430\u0451\u0442 \u0441\u043e\u0442\u043d\u0438 \u0441\u0442\u0430\u0442\u0435\u0439 \u043f\u0440\u043e \u00ab\u0443\u0447\u0451\u043d\u044b\u0445, \u0434\u043e\u043a\u0430\u0437\u0430\u0432\u0448\u0438\u0445 \u0432\u0440\u0435\u0434 \u044d\u043b\u0435\u043a\u0442\u0440\u0438\u0447\u0435\u0441\u0442\u0432\u0430\u00bb \u2014 \u043d\u0435 \u0434\u043b\u044f \u0444\u0435\u0439\u043a\u043e\u0432\u043e\u0433\u043e \u0441\u0440\u0430\u0447\u0430 \u0432 \u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0445, \u0430 \u0447\u0442\u043e\u0431\u044b \u043e\u043d\u0438 \u043f\u043e\u043f\u0430\u043b\u0438 \u0432 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f LLM.<\/li>\n \t<li>\u0412 TikTok \u043f\u043e\u044f\u0432\u043b\u044f\u044e\u0442\u0441\u044f \"\u0434\u043e\u043a\u0430\u0437\u0430\u0442\u0435\u043b\u044c\u0441\u0442\u0432\u0430\", \u0447\u0442\u043e \u00ab\u041f\u043e\u043b\u044c\u0448\u0430 \u0431\u044b\u043b\u0430 \u0447\u0430\u0441\u0442\u044c\u044e \u0420\u043e\u0441\u0441\u0438\u0438\u00bb \u2014 \u043e\u043f\u044f\u0442\u044c \u0436\u0435, \u043d\u0435 \u0440\u0430\u0434\u0438 \u043f\u0440\u043e\u0441\u043c\u043e\u0442\u0440\u043e\u0432, \u0430 \u0440\u0430\u0434\u0438 \u0438\u043d\u0434\u0435\u043a\u0441\u0430\u0446\u0438\u0438.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f \u041a\u0430\u043a \u0432\u044b\u044f\u0432\u0438\u0442\u044c LLM Grooming? (\u043c\u0435\u0442\u043e\u0434\u044b \u043e\u0441\u043e\u0437\u043d\u0430\u043d\u043d\u043e\u0433\u043e \u0430\u0443\u0434\u0438\u0442\u0430)\n<ol start=\"1\" data-spread=\"true\">\n \t<li>\u0421\u043b\u0443\u0448\u0430\u0439, \u0447\u0442\u043e \u0433\u043e\u0432\u043e\u0440\u044f\u0442 \u043c\u043e\u0434\u0435\u043b\u0438\n<ul data-spread=\"false\">\n \t<li>\u0417\u0430\u0434\u0430\u0432\u0430\u0439 \u0447\u0443\u0432\u0441\u0442\u0432\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b \u0438 \u0441\u043c\u043e\u0442\u0440\u0438, \u043d\u0435 \u043a\u043b\u043e\u043d\u0438\u0442 \u043b\u0438 \u043e\u0442\u0432\u0435\u0442 \u0432 \u043e\u0434\u043d\u0443 \u0441\u0442\u043e\u0440\u043e\u043d\u0443, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0435\u0441\u043b\u0438 \u044d\u0442\u043e \u043d\u0435 \u0441\u043e\u043e\u0442\u0432\u0435\u0442\u0441\u0442\u0432\u0443\u0435\u0442 \u0431\u0430\u043b\u0430\u043d\u0441\u0443 \u0432 \u0440\u0435\u0430\u043b\u044c\u043d\u043e\u043c \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u043d\u043e\u043c \u0441\u043e\u043e\u0431\u0449\u0435\u0441\u0442\u0432\u0435.<\/li>\n \t<li>\u0421\u0440\u0430\u0432\u043d\u0438\u0432\u0430\u0439 \u043f\u043e\u0432\u0435\u0434\u0435\u043d\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043e\u0442 \u0440\u0430\u0437\u043d\u044b\u0445 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432: GPT, Claude, Gemini. \u0415\u0441\u043b\u0438 \u0432\u0441\u0435 \u00ab\u0432\u0435\u0434\u0443\u0442\u0441\u044f\u00bb \u043d\u0430 \u043e\u0434\u0438\u043d \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432 \u2014 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u043e, \u043e\u043d \u043f\u0440\u043e\u0448\u0451\u043b \u0447\u0435\u0440\u0435\u0437 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u0421\u043b\u0435\u0434\u0438 \u0437\u0430 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0430\u043c\u0438\n<ul data-spread=\"false\">\n \t<li>\u0415\u0441\u043b\u0438 \u043c\u043e\u0434\u0435\u043b\u044c \u0441\u0441\u044b\u043b\u0430\u0435\u0442\u0441\u044f \u043d\u0430 \u043c\u0430\u0440\u0433\u0438\u043d\u0430\u043b\u044c\u043d\u044b\u0435 \u0438\u043b\u0438 \u043e\u0434\u043d\u043e\u0442\u0438\u043f\u043d\u044b\u0435 \u0441\u0430\u0439\u0442\u044b \u2014 \u0442\u0440\u0435\u0432\u043e\u0436\u043d\u044b\u0439 \u0437\u0432\u043e\u043d\u043e\u0447\u0435\u043a.<\/li>\n \t<li>\u041c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043f\u0440\u043e\u0441\u043b\u043e\u0439\u043a\u0443 \u0430\u043d\u0430\u043b\u0438\u0437\u0430 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043f\u043e\u0434\u043a\u043b\u044e\u0447\u0438\u0442\u044c \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 trafilatura, newspaper3k) \u0438 \u043f\u043e\u0441\u043c\u043e\u0442\u0440\u0435\u0442\u044c, \u043a\u0430\u043a\u0438\u0435 \u0434\u043e\u043c\u0435\u043d\u044b \u0432\u0441\u043f\u043b\u044b\u0432\u0430\u044e\u0442 \u0447\u0430\u0449\u0435 \u0432\u0441\u0435\u0433\u043e.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u0421\u043c\u043e\u0442\u0440\u0438 \u043d\u0430 \u0448\u0443\u043c \u0432 \u0438\u043d\u0444\u043e\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0441\u0442\u0432\u0435\n<ul data-spread=\"false\">\n \t<li>\u0415\u0441\u043b\u0438 \u0432\u0434\u0440\u0443\u0433 \u043f\u043e\u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u0432\u0441\u043f\u043b\u0435\u0441\u043a \u043f\u043e\u0445\u043e\u0436\u0438\u0445 \u0442\u0435\u043a\u0441\u0442\u043e\u0432, \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 \u0438\u043b\u0438 \u0442\u0435\u0440\u043c\u0438\u043d\u043e\u0432 \u2014 \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u043f\u043e\u043f\u044b\u0442\u043a\u0430 \u043c\u0430\u0441\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u043d\u043e\u0433\u043e \u0432\u0431\u0440\u043e\u0441\u0430 \u0432 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u0443\u044e \u0441\u0438\u0441\u0442\u0435\u043c\u0443 \u0438, \u0447\u0435\u0440\u0435\u0437 \u043d\u0435\u0451, \u0432 \u0431\u0443\u0434\u0443\u0449\u0435\u0435 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 LLM.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\ud83e\uddf7 \u041a\u0430\u043a \u0437\u0430\u0449\u0438\u0449\u0430\u0442\u044c\u0441\u044f? (\u0435\u0441\u043b\u0438 \u0442\u044b \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c, \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a, \u0438\u043b\u0438 \u043f\u0440\u043e\u0441\u0442\u043e \u043d\u0435\u0440\u0430\u0432\u043d\u043e\u0434\u0443\u0448\u043d\u044b\u0439) \ud83d\udd12 \u0415\u0441\u043b\u0438 \u0442\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0448\u044c \u0441 \u0418\u0418:\n<ul data-spread=\"false\">\n \t<li>\u0421\u043b\u0435\u0434\u0438 \u0437\u0430 \u043c\u0435\u0442\u0430-\u043a\u043e\u043d\u0442\u0435\u043d\u0442\u043e\u043c: \u043a\u0430\u043a\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u043f\u043e\u043f\u0430\u0434\u0430\u044e\u0442 \u0432 \u0442\u0432\u043e\u0438 \u043c\u043e\u0434\u0435\u043b\u0438? \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0439 \u0444\u0438\u043b\u044c\u0442\u0440\u044b, \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0439 \u0434\u043e\u043c\u0435\u043d\u044b, \u0432\u0430\u043b\u0438\u0434\u0438\u0440\u0443\u0439 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438.<\/li>\n \t<li>\u0414\u043e\u0431\u0430\u0432\u043b\u044f\u0439 \u0441\u043b\u043e\u0438 \u0444\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433\u0430: \u0444\u0438\u043b\u044c\u0442\u0440\u044b \u0442\u0438\u043f\u0430 ClaimBuster, \u043c\u043e\u0434\u0435\u043b\u0438 \u0442\u0438\u043f\u0430 TrustworthyQA.<\/li>\n<\/ul>\n\ud83d\udd75\ufe0f \u0415\u0441\u043b\u0438 \u0442\u044b \u0436\u0443\u0440\u043d\u0430\u043b\u0438\u0441\u0442, \u0430\u043a\u0442\u0438\u0432\u0438\u0441\u0442 \u0438\u043b\u0438 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a:\n<ul data-spread=\"false\">\n \t<li>\u0412\u043d\u0435\u0434\u0440\u0438 \u043c\u043e\u043d\u0438\u0442\u043e\u0440\u0438\u043d\u0433 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0442\u0435\u043c \u0432 Google, TikTok, Telegram, YouTube.<\/li>\n \t<li>\u0421\u043c\u043e\u0442\u0440\u0438, \u043d\u0435 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u044e\u0442\u0441\u044f \u043b\u0438 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0435 \u0444\u0440\u0430\u0437\u044b, \u043f\u0430\u0442\u0442\u0435\u0440\u043d\u044b \u0444\u043e\u0440\u043c\u0443\u043b\u0438\u0440\u043e\u0432\u043e\u043a \u2014 \u044d\u0442\u043e \u043c\u043e\u0436\u0435\u0442 \u0431\u044b\u0442\u044c \u0441\u0438\u0433\u043d\u0430\u043b\u043e\u043c \"\u0438\u043d\u0444\u043e\u0437\u0430\u0441\u0435\u0432\u0430\".<\/li>\n<\/ul>\n\ud83d\udcac \u0415\u0441\u043b\u0438 \u0442\u044b \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044c:\n<ul data-spread=\"false\">\n \t<li>\u0411\u0443\u0434\u044c \u0441\u043a\u0435\u043f\u0442\u0438\u0447\u043d\u044b\u043c. \u0414\u0430\u0436\u0435 \u0435\u0441\u043b\u0438 \u0418\u0418 \u0447\u0442\u043e-\u0442\u043e \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0430\u0435\u0442 \u2014 \u043f\u0440\u043e\u0432\u0435\u0440\u044c.<\/li>\n \t<li>\u041f\u043e\u043c\u043d\u0438: \u0418\u0418 \u043c\u043e\u0436\u0435\u0442 \u00ab\u043f\u043e\u0432\u0442\u043e\u0440\u044f\u0442\u044c\u00bb \u0437\u0430 \u0442\u0435\u043c\u0438, \u043a\u0442\u043e \u0433\u0440\u043e\u043c\u0447\u0435, \u0430 \u043d\u0435 \u0437\u0430 \u0442\u0435\u043c\u0438, \u043a\u0442\u043e \u043f\u0440\u0430\u0432.<\/li>\n<\/ul>\n\ud83d\udccc \u0427\u0442\u043e \u0434\u0430\u043b\u044c\u0448\u0435?\n<ul data-spread=\"false\">\n \t<li>LLM grooming \u2014 \u044d\u0442\u043e \u043d\u043e\u0432\u044b\u0439 \u0432\u0435\u043a\u0442\u043e\u0440 FIMI (foreign information manipulation and interference). \u041e\u043d \u043d\u0435\u0437\u0430\u043c\u0435\u0442\u0435\u043d, \u0442\u0438\u0445, \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442 \u043d\u0430 \u0434\u043e\u043b\u0433\u0443\u044e \u0438\u0433\u0440\u0443.<\/li>\n \t<li>\u0418\u043c\u0435\u043d\u043d\u043e \u043f\u043e\u044d\u0442\u043e\u043c\u0443 \u0432\u0430\u0436\u043d\u043e \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u0437\u0430\u0449\u0438\u0449\u0430\u0442\u044c \u0418\u0418 \u043e\u0442 \u0444\u0435\u0439\u043a\u043e\u0432, \u043d\u043e \u0438 \u0443\u0447\u0438\u0442\u044c \u0435\u0433\u043e \u043a\u0440\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043c\u044b\u0441\u043b\u0438\u0442\u044c \u2014 \u0442\u0430\u043a \u0436\u0435, \u043a\u0430\u043a \u043b\u044e\u0434\u0435\u0439.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u041a\u0435\u0439\u0441 Pravda Network: \u0441\u0435\u0442\u0438 \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0435\u0439<\/strong>\n\u0412 <a href=\"https:\/\/static1.squarespace.com\/static\/6612cbdfd9a9ce56ef931004\/t\/67fd396818196f3d1666bc23\/1744648558879\/PK+Report.pdf\">\u043e\u0442\u0447\u0451\u0442\u0435<\/a> American Sunlight Project (\u0444\u0435\u0432\u0440\u0430\u043b\u044c 2025) \u043e\u043f\u0438\u0441\u0430\u043d\u0430 \u0434\u0435\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0441\u0435\u0442\u0438 Pravda Network \u2014 \u0447\u0430\u0441\u0442\u0438 \u0431\u043e\u043b\u0435\u0435 \u0448\u0438\u0440\u043e\u043a\u043e\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u043f\u043e\u0434 \u043d\u0430\u0437\u0432\u0430\u043d\u0438\u0435\u043c \"Portal Kombat\". \u042d\u0442\u0430 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 \u0434\u043e\u043c\u0435\u043d\u044b \u0438 \u043f\u043e\u0434\u0434\u043e\u043c\u0435\u043d\u044b, \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u044e\u0449\u0438\u0435 \u0438\u0434\u0435\u043d\u0442\u0438\u0447\u043d\u044b\u0435 \u0442\u0435\u043a\u0441\u0442\u044b \u0441 \u043f\u0440\u043e\u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0438\u043c\u0438 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u0430\u043c\u0438, \u043d\u0430 \u0440\u0430\u0437\u043d\u044b\u0445 \u044f\u0437\u044b\u043a\u0430\u0445 \u0438 \u043f\u043e\u0434 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0431\u0440\u0435\u043d\u0434\u0430\u043c\u0438.\n\n\u042d\u0442\u0438 \u0441\u0430\u0439\u0442\u044b:\n<ul data-spread=\"false\">\n \t<li>\u043e\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u044b \u043a\u0430\u043a \u043d\u043e\u0432\u043e\u0441\u0442\u043d\u044b\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438,<\/li>\n \t<li>\u0438\u043c\u0435\u044e\u0442 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u043d\u0443\u044e \u0444\u0443\u043d\u043a\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0434\u043b\u044f \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430 (\u043f\u043b\u043e\u0445\u0430\u044f \u043d\u0430\u0432\u0438\u0433\u0430\u0446\u0438\u044f, \u043d\u0435\u0443\u0434\u043e\u0431\u043d\u043e\u0435 \u043e\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u0438\u0435),<\/li>\n \t<li>\u0441\u043a\u043e\u0440\u0435\u0435 \u0432\u0441\u0435\u0433\u043e, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u044b \u0434\u043b\u044f \u0438\u043d\u0434\u0435\u043a\u0441\u0430\u0446\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u043c\u0438 \u043c\u043e\u0434\u0435\u043b\u044f\u043c\u0438.<\/li>\n<\/ul>\n\u0421\u043e\u0433\u043b\u0430\u0441\u043d\u043e \u0434\u0430\u043d\u043d\u044b\u043c \u0434\u0430\u0448\u0431\u043e\u0440\u0434\u0430 <a href=\"https:\/\/portal-kombat.com\/\">portal-kombat.com<\/a>, \u0441\u0435\u0442\u044c \u0432\u043a\u043b\u044e\u0447\u0430\u0435\u0442 182 \u0441\u0430\u0439\u0442\u0430, \u0441\u043e\u0432\u043c\u0435\u0449\u0430\u044e\u0449\u0438\u0445 \u043e\u0434\u043d\u0438 \u0438 \u0442\u0435 \u0436\u0435 \u0442\u0435\u043a\u0441\u0442\u044b \u0441 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u044f\u0437\u044b\u043a\u043e\u0432\u044b\u043c\u0438 \u043c\u0435\u0442\u0430\u0434\u0430\u043d\u043d\u044b\u043c\u0438.\n\n<img class=\"alignnone size-full wp-image-3344\" src=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png\" alt=\"\" width=\"2560\" height=\"1311\" \/>\n\n\u0414\u0430\u0448\u0431\u043e\u0440\u0434 \u043e\u0442\u043e\u0431\u0440\u0430\u0436\u0430\u0435\u0442 \u0441\u043f\u0438\u0441\u043e\u043a \u0434\u043e\u043c\u0435\u043d\u043e\u0432, \u0438\u0445 \u0434\u0430\u0442\u0443 \u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0430\u0446\u0438\u0438, \u0437\u0430\u0440\u0435\u0433\u0438\u0441\u0442\u0440\u0438\u0440\u043e\u0432\u0430\u0432\u0448\u0443\u044e \u0441\u0442\u0440\u0430\u043d\u0443 \u0438 \u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u043e\u043d\u043d\u0443\u044e \"\u0441\u0444\u0435\u0440\u0443\" (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043d\u0430\u0446\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u0443\u044e \u043f\u0440\u0438\u043d\u0430\u0434\u043b\u0435\u0436\u043d\u043e\u0441\u0442\u044c \u0441\u0430\u0439\u0442\u0430). \u042d\u0442\u043e \u0438\u043d\u0442\u0435\u0440\u0430\u043a\u0442\u0438\u0432\u043d\u044b\u0439 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044f\u043c \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u0442\u044c \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0443 \u0441\u0435\u0442\u0438, \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u044b \u043e\u0445\u0432\u0430\u0442\u0430 \u0438 \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u043e\u0432.\n<div><\/div>\n<strong>\u041c\u043e\u0442\u0438\u0432\u044b \u0441\u0435\u0442\u0438<\/strong>\n\u0412 \u043f\u0440\u043e\u0448\u043b\u044b\u0445 \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u044f\u0445 \u043e \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u044b\u0445 \u043c\u043e\u0442\u0438\u0432\u0430\u0445 \u0441\u0435\u0442\u0438 \u00ab\u041f\u0440\u0430\u0432\u0434\u0430\u00bb \u043e\u0441\u043d\u043e\u0432\u043d\u043e\u0435 \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u0443\u0434\u0435\u043b\u044f\u043b\u043e\u0441\u044c \u0435\u0451 \u0430\u043d\u0442\u0438\u0443\u043a\u0440\u0430\u0438\u043d\u0441\u043a\u043e\u043c\u0443 \u0438 \u043f\u0440\u043e\u0432\u043e\u0435\u043d\u043d\u043e\u043c\u0443 \u0445\u0430\u0440\u0430\u043a\u0442\u0435\u0440\u0443, \u0430 \u0442\u0430\u043a\u0436\u0435 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u043c \u043f\u043e\u0441\u043b\u0435\u0434\u0441\u0442\u0432\u0438\u044f\u043c \u0434\u043b\u044f \u0435\u0432\u0440\u043e\u043f\u0435\u0439\u0441\u043a\u0438\u0445 \u0432\u044b\u0431\u043e\u0440\u043e\u0432 2024 \u0433\u043e\u0434\u0430. \u041e\u0434\u043d\u0430\u043a\u043e, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u044d\u0442\u0430 \u0441\u0435\u0442\u044c \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0430\u0435\u0442 \u0440\u0430\u0441\u0442\u0438 \u0438 \u043c\u0435\u043d\u044f\u0442\u044c\u0441\u044f, \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u043e \u0431\u043e\u043b\u0435\u0435 \u0442\u0449\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u0438\u0437\u0443\u0447\u0435\u043d\u0438\u0435, \u0447\u0442\u043e\u0431\u044b \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0438\u0442\u044c \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u0443\u044e \u0442\u0440\u0430\u0435\u043a\u0442\u043e\u0440\u0438\u044e \u0435\u0451 \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u044f. ASP \u0440\u0430\u0441\u0441\u043c\u0430\u0442\u0440\u0438\u0432\u0430\u0435\u0442 \u0442\u0440\u0438 \u0432\u043e\u0437\u043c\u043e\u0436\u043d\u044b\u0445, \u043d\u0435 \u0438\u0441\u043a\u043b\u044e\u0447\u0430\u044e\u0449\u0438\u0445 \u0434\u0440\u0443\u0433 \u0434\u0440\u0443\u0433\u0430 \u043c\u043e\u0442\u0438\u0432\u0430 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0441\u0435\u0442\u0438, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0441\u043e\u0441\u0440\u0435\u0434\u043e\u0442\u043e\u0447\u0435\u043d\u044b \u043d\u0430 \u0435\u0451 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e\u0441\u0442\u044f\u0445 \u0438 \u043d\u0435\u0434\u043e\u0441\u0442\u0430\u0442\u043a\u0430\u0445. \u042d\u0442\u0438 \u043c\u043e\u0442\u0438\u0432\u044b \u043d\u0435 \u043f\u0440\u0438\u0432\u044f\u0437\u0430\u043d\u044b \u043a \u043a\u043e\u043d\u043a\u0440\u0435\u0442\u043d\u044b\u043c \u0441\u0442\u0440\u0430\u043d\u0430\u043c, \u0440\u0435\u0433\u0438\u043e\u043d\u0430\u043c \u0438\u043b\u0438 \u043f\u043e\u043b\u0438\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u043c \u0441\u043e\u0431\u044b\u0442\u0438\u044f\u043c, \u043f\u043e\u0441\u043a\u043e\u043b\u044c\u043a\u0443 \u0446\u0435\u043b\u0438 \u043f\u0440\u043e\u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0438\u0445 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u043c\u043e\u0433\u0443\u0442 \u043c\u0435\u043d\u044f\u0442\u044c\u0441\u044f.\n<strong>\u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 A: \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0430 LLM<\/strong>\n\u041d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0437\u043d\u0430\u0447\u0438\u043c\u044b\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u0438\u0441\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u043d\u0438\u044f ASP \u0441\u0442\u0430\u043b\u043e \u043d\u0435 \u0440\u0430\u0441\u0448\u0438\u0440\u0435\u043d\u0438\u0435 \u0441\u0435\u0442\u0438 \u0438\u043b\u0438 \u0435\u0451 \u043e\u0440\u0438\u0435\u043d\u0442\u0430\u0446\u0438\u044f \u043d\u0430 \u043d\u0435\u0437\u0430\u043f\u0430\u0434\u043d\u044b\u0435 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0430, \u0430 \u043c\u043e\u0434\u0435\u043b\u044c \u0431\u0443\u0434\u0443\u0449\u0438\u0445 \u0438\u043d\u0444\u043e\u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439, \u043f\u043e\u0441\u0442\u0440\u043e\u0435\u043d\u043d\u044b\u0445 \u043d\u0430 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0437\u0430\u0446\u0438\u0438. \u0421\u0435\u0442\u044c \u00ab\u041f\u0440\u0430\u0432\u0434\u0430\u00bb \u2014 \u043e\u0433\u0440\u043e\u043c\u043d\u0430\u044f, \u0431\u044b\u0441\u0442\u0440\u043e\u0440\u0430\u0441\u0442\u0443\u0449\u0430\u044f, \u043d\u0435\u0443\u0434\u043e\u0431\u043d\u0430\u044f \u0434\u043b\u044f \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044f, \u2014 \u0441\u043a\u043e\u0440\u0435\u0435 \u0432\u0441\u0435\u0433\u043e, \u0440\u0430\u0441\u0441\u0447\u0438\u0442\u0430\u043d\u0430 \u043d\u0430 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u0430\u0433\u0435\u043d\u0442\u043e\u0432: \u0432\u0435\u0431-\u043a\u0440\u0430\u0443\u043b\u0435\u0440\u044b, \u0441\u043a\u0440\u0430\u043f\u043f\u0435\u0440\u044b \u0438 \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c\u044b, \u0444\u043e\u0440\u043c\u0438\u0440\u0443\u044e\u0449\u0438\u0435 LLM. \u042d\u0442\u043e \u043c\u0430\u0441\u0441\u043e\u0432\u043e\u0435 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0441\u0442\u0432\u043e \u0438 \u0434\u0443\u0431\u043b\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435 \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u0430 \u0441 \u0446\u0435\u043b\u044c\u044e \u043f\u043e\u043f\u0430\u0441\u0442\u044c \u0432 \u0431\u0443\u0434\u0443\u0449\u0438\u0435 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b \u0418\u0418.\n\nASP \u043d\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0442\u0430\u043a\u0443\u044e \u0442\u0430\u043a\u0442\u0438\u043a\u0443 <strong>LLM grooming<\/strong> \u2014 \u043f\u0440\u0435\u0434\u043d\u0430\u043c\u0435\u0440\u0435\u043d\u043d\u043e\u0435 \u043d\u0430\u0441\u044b\u0449\u0435\u043d\u0438\u0435 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0430 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0435\u0439, \u043f\u0440\u0435\u0434\u043d\u0430\u0437\u043d\u0430\u0447\u0435\u043d\u043d\u043e\u0439 \u0434\u043b\u044f \u043f\u043e\u0442\u0440\u0435\u0431\u043b\u0435\u043d\u0438\u044f \u043c\u0430\u0448\u0438\u043d\u0430\u043c\u0438. \u0412 \u0438\u044e\u043d\u0435 2024 \u0433\u043e\u0434\u0430 NewsGuard \u043f\u043e\u043a\u0430\u0437\u0430\u043b, \u0447\u0442\u043e \u0432\u0435\u0434\u0443\u0449\u0438\u0435 LLM \u0432 \u0441\u0440\u0435\u0434\u043d\u0435\u043c \u0432 31,8 % \u0441\u043b\u0443\u0447\u0430\u0435\u0432 \u0432\u043e\u0441\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u044f\u0442 \u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0443\u044e \u0434\u0435\u0437\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e. \u0415\u0441\u043b\u0438 \u043d\u0435 \u043f\u0440\u0438\u043d\u044f\u0442\u044c \u043c\u0435\u0440\u044b, LLM grooming \u043f\u0440\u0435\u0434\u0441\u0442\u0430\u0432\u043b\u044f\u0435\u0442 \u0443\u0433\u0440\u043e\u0437\u0443 \u0446\u0435\u043b\u043e\u0441\u0442\u043d\u043e\u0441\u0442\u0438 \u043e\u0442\u043a\u0440\u044b\u0442\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0430.\n\n\u0424\u0435\u0432\u0440\u0430\u043b\u044c 2023 \u0433\u043e\u0434\u0430 \u2014 \u0434\u0430\u0442\u0430 \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0441\u0435\u0442\u0438 \u00ab\u041f\u0440\u0430\u0432\u0434\u0430\u00bb \u2014 \u0441\u043e\u0432\u043f\u0430\u0434\u0430\u0435\u0442 \u0441 \u043c\u043e\u043c\u0435\u043d\u0442\u043e\u043c \u043f\u043e\u043f\u0443\u043b\u044f\u0440\u0438\u0437\u0430\u0446\u0438\u0438 \u0433\u0435\u043d\u0435\u0440\u0430\u0442\u0438\u0432\u043d\u043e\u0433\u043e \u0418\u0418. \u0420\u0430\u043d\u0435\u0435 \u0443\u0436\u0435 \u0444\u0438\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043b\u0438\u0441\u044c \u043f\u043e\u043f\u044b\u0442\u043a\u0438 \u043f\u0440\u0438\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u044f \u043a\u0440\u0430\u0443\u043b\u0435\u0440\u043e\u0432 \u0447\u0435\u0440\u0435\u0437 SEO-\u043e\u043f\u0442\u0438\u043c\u0438\u0437\u0430\u0446\u0438\u044e. \u0412 \u043e\u0442\u043b\u0438\u0447\u0438\u0435 \u043e\u0442 \u0442\u0440\u0430\u0434\u0438\u0446\u0438\u043e\u043d\u043d\u043e\u0433\u043e SEO, \u0446\u0435\u043b\u044c LLM grooming \u2014 \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u043e\u0432\u044b\u0441\u0438\u0442\u044c \u0432\u0438\u0434\u0438\u043c\u043e\u0441\u0442\u044c, \u0430 <strong>\u0437\u0430\u043f\u0440\u043e\u0433\u0440\u0430\u043c\u043c\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0418\u0418<\/strong> \u043d\u0430 \u043f\u043e\u0432\u0442\u043e\u0440\u0435\u043d\u0438\u0435 \u043e\u043f\u0440\u0435\u0434\u0435\u043b\u0451\u043d\u043d\u044b\u0445 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u043e\u0432. \u042d\u0442\u043e \u043f\u043e\u043a\u0430 \u043c\u0430\u043b\u043e\u0438\u0437\u0443\u0447\u0435\u043d\u043d\u0430\u044f \u0443\u0433\u0440\u043e\u0437\u0430.\n<strong>\u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 B: \u043c\u0430\u0441\u0441\u043e\u0432\u043e\u0435 \u043d\u0430\u0441\u044b\u0449\u0435\u043d\u0438\u0435<\/strong>\n\u0421\u0435\u0442\u044c \u0435\u0436\u0435\u0434\u043d\u0435\u0432\u043d\u043e \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u0435\u0442 \u043e\u0433\u0440\u043e\u043c\u043d\u043e\u0435 \u043a\u043e\u043b\u0438\u0447\u0435\u0441\u0442\u0432\u043e \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u043e\u0432, \u043d\u0430\u0441\u044b\u0449\u0430\u044f \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442 \u043f\u0440\u043e\u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0438\u043c \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u043e\u043c. \u042d\u0442\u043e \u0443\u0432\u0435\u043b\u0438\u0447\u0438\u0432\u0430\u0435\u0442:\n<ul data-spread=\"false\">\n \t<li>\u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c \u0442\u043e\u0433\u043e, \u0447\u0442\u043e \u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u0435\u043b\u044c \u043d\u0430\u0442\u043a\u043d\u0451\u0442\u0441\u044f \u043d\u0430 \u043d\u0443\u0436\u043d\u044b\u0439 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432,<\/li>\n \t<li>\u0448\u0430\u043d\u0441, \u0447\u0442\u043e \u0432\u043d\u0435\u0448\u043d\u0438\u0435 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0412\u0438\u043a\u0438\u043f\u0435\u0434\u0438\u044f) \u0431\u0443\u0434\u0443\u0442 \u0441\u0441\u044b\u043b\u0430\u0442\u044c\u0441\u044f \u043d\u0430 \u044d\u0442\u0438 \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u044b.<\/li>\n<\/ul>\n\u041c\u0435\u0445\u0430\u043d\u0438\u0437\u043c \u043c\u0430\u0441\u0441\u043e\u0432\u043e\u0433\u043e \u0432\u043e\u0437\u0434\u0435\u0439\u0441\u0442\u0432\u0438\u044f \u0444\u043e\u0440\u043c\u0438\u0440\u0443\u0435\u0442 <strong>\u044d\u0444\u0444\u0435\u043a\u0442 \u0438\u043b\u043b\u044e\u0437\u0438\u0438 \u043f\u0440\u0430\u0432\u0434\u044b<\/strong>: \u0447\u0435\u043c \u0447\u0430\u0449\u0435 \u0447\u0435\u043b\u043e\u0432\u0435\u043a \u0441\u0442\u0430\u043b\u043a\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u0441 \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u0435\u043c, \u0442\u0435\u043c \u0432\u044b\u0448\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c, \u0447\u0442\u043e \u043e\u043d \u0432 \u043d\u0435\u0433\u043e \u043f\u043e\u0432\u0435\u0440\u0438\u0442.\n<strong>\u041e\u0431\u044a\u044f\u0441\u043d\u0435\u043d\u0438\u0435 C: \u044d\u0444\u0444\u0435\u043a\u0442 \u0438\u043b\u043b\u044e\u0437\u043e\u0440\u043d\u043e\u0439 \u043f\u0440\u0430\u0432\u0434\u044b \u0438\u0437 \u043d\u0435\u0441\u043a\u043e\u043b\u044c\u043a\u0438\u0445 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432<\/strong>\n\u0421\u0435\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u044f\u0435\u0442 \u043e\u0434\u0438\u043d \u0438 \u0442\u043e\u0442 \u0436\u0435 \u043a\u043e\u043d\u0442\u0435\u043d\u0442 \u0447\u0435\u0440\u0435\u0437 \u043c\u043d\u043e\u0436\u0435\u0441\u0442\u0432\u043e \u043a\u0430\u043d\u0430\u043b\u043e\u0432: \u0441\u0430\u0439\u0442\u044b, Telegram, X, VK \u0438 \u0434\u0430\u0436\u0435 Bluesky. \u042d\u0442\u043e \u0441\u043e\u0437\u0434\u0430\u0451\u0442 \u0438\u043b\u043b\u044e\u0437\u0438\u044e \u043f\u043e\u0434\u0442\u0432\u0435\u0440\u0436\u0434\u0451\u043d\u043d\u043e\u0439 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438 \u0438\u0437 \u00ab\u0440\u0430\u0437\u043d\u044b\u0445\u00bb \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432. \u0412 \u0434\u0435\u043b\u043e \u0432\u0441\u0442\u0443\u043f\u0430\u0435\u0442 \u043a\u0430\u043a \u043f\u0440\u0435\u0434\u043d\u0430\u043c\u0435\u0440\u0435\u043d\u043d\u043e\u0435 \u00ab\u043e\u0442\u043c\u044b\u0432\u0430\u043d\u0438\u0435\u00bb \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043a\u043e\u0433\u0434\u0430 \u043d\u0430 \u0441\u0435\u0442\u044c \u0441\u0441\u044b\u043b\u0430\u044e\u0442\u0441\u044f \u0434\u0440\u0443\u0433\u0438\u0435 \u043f\u0440\u043e\u0440\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0438\u0435 \u0440\u0435\u0441\u0443\u0440\u0441\u044b), \u0442\u0430\u043a \u0438 \u043d\u0435\u043f\u0440\u0435\u0434\u043d\u0430\u043c\u0435\u0440\u0435\u043d\u043d\u043e\u0435 (\u043a\u043e\u0433\u0434\u0430 \u0443\u0432\u0430\u0436\u0430\u0435\u043c\u0430\u044f \u043e\u0440\u0433\u0430\u043d\u0438\u0437\u0430\u0446\u0438\u044f \u0438\u043b\u0438 \u043b\u0438\u0446\u043e \u0434\u0435\u043b\u044f\u0442\u0441\u044f \u0441\u0441\u044b\u043b\u043a\u043e\u0439, \u043d\u0435 \u0437\u043d\u0430\u044f \u043e \u0435\u0451 \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u0438).\n\n\u0412\u0441\u0435 \u0442\u0440\u0438 \u043c\u043e\u0442\u0438\u0432\u0430 \u0443\u0441\u0438\u043b\u0438\u0432\u0430\u044e\u0442 \u0434\u0440\u0443\u0433 \u0434\u0440\u0443\u0433\u0430. \u0427\u0435\u043c \u0431\u043e\u043b\u044c\u0448\u0435 \u0441\u0442\u0440\u0430\u043d\u0438\u0446, URL \u0438 \u043f\u0435\u0440\u0435\u0432\u043e\u0434\u043e\u0432 \u0441\u043e\u0437\u0434\u0430\u0451\u0442 \u0441\u0435\u0442\u044c, \u0442\u0435\u043c \u0432\u044b\u0448\u0435 \u0432\u0435\u0440\u043e\u044f\u0442\u043d\u043e\u0441\u0442\u044c, \u0447\u0442\u043e \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u044b \u0431\u0443\u0434\u0443\u0442 \u043f\u0440\u0438\u043d\u044f\u0442\u044b \u0438 \u043b\u044e\u0434\u044c\u043c\u0438, \u0438 \u043c\u0430\u0448\u0438\u043d\u0430\u043c\u0438. \u0425\u043e\u0442\u044f \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u043e \u0441\u0430\u0439\u0442\u043e\u0432 \u043d\u0438\u0437\u043a\u043e\u0435, \u044d\u0442\u043e \u043d\u0435 \u043c\u0435\u0448\u0430\u0435\u0442 \u0438\u043c \u0441\u0442\u0430\u043d\u043e\u0432\u0438\u0442\u044c\u0441\u044f \u0447\u0430\u0441\u0442\u044c\u044e \u0446\u0438\u0444\u0440\u043e\u0432\u043e\u0433\u043e \u0441\u043b\u0435\u0434\u0430, \u0443\u0447\u0438\u0442\u044b\u0432\u0430\u0435\u043c\u043e\u0433\u043e LLM.\n<div>\n\n<iframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/kk7tBTg_Xbo?si=GANrkylWs1tFMyKi\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n\n<\/div>\n<strong>\u0421\u0446\u0435\u043d\u0430\u0440\u0438\u0438 LLM-grooming<\/strong>\n\u0410\u0432\u0442\u043e\u0440\u044b \u0434\u043e\u043a\u043b\u0430\u0434\u0430 \u0432\u044b\u0434\u0435\u043b\u044f\u044e\u0442 \u0442\u0440\u0438 \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0435 \u0446\u0435\u043b\u0438 \u0442\u0430\u043a\u0438\u0445 \u0441\u0435\u0442\u0435\u0439:\n<ol start=\"1\" data-spread=\"false\">\n \t<li><strong>\u0412\u043a\u043b\u044e\u0447\u0435\u043d\u0438\u0435 \u0432 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b<\/strong> \u2014 \u0441\u0430\u0439\u0442\u044b \u0438\u043d\u0434\u0435\u043a\u0441\u0438\u0440\u0443\u044e\u0442\u0441\u044f \u0432 \u043f\u043e\u0438\u0441\u043a\u043e\u0432\u044b\u0445 \u0441\u0438\u0441\u0442\u0435\u043c\u0430\u0445 \u0438 \u043f\u043e\u043f\u0430\u0434\u0430\u044e\u0442 \u0432 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 LLM, \u0432\u043d\u0435\u0434\u0440\u044f\u044f \u043f\u0440\u043e\u043a\u0440\u0435\u043c\u043b\u0451\u0432\u0441\u043a\u0438\u0435 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u044b \u0432 \u0430\u0440\u0445\u0438\u0442\u0435\u043a\u0442\u0443\u0440\u0443 \u043c\u043e\u0434\u0435\u043b\u0438.<\/li>\n \t<li><strong>\u0421\u043e\u0437\u0434\u0430\u043d\u0438\u0435 \u0438\u043b\u043b\u044e\u0437\u0438\u0438 \u043d\u0435\u0437\u0430\u0432\u0438\u0441\u0438\u043c\u044b\u0445 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u043e\u0432<\/strong> \u2014 \u043e\u0434\u0438\u043d \u0438 \u0442\u043e\u0442 \u0436\u0435 \u0442\u0435\u043a\u0441\u0442 \u0440\u0430\u0437\u043c\u0435\u0449\u0430\u0435\u0442\u0441\u044f \u043d\u0430 \u0441\u043e\u0442\u043d\u044f\u0445 \u0441\u0430\u0439\u0442\u043e\u0432, \u0447\u0442\u043e \u0441\u043e\u0437\u0434\u0430\u0451\u0442 \u044d\u0444\u0444\u0435\u043a\u0442 \"\u043a\u043e\u043d\u0441\u0435\u043d\u0441\u0443\u0441\u0430\".<\/li>\n \t<li><strong>\u0420\u0430\u0437\u043c\u044b\u0442\u0438\u0435 \u0438\u043d\u0444\u043e\u043f\u043e\u043b\u044f<\/strong> \u2014 LLM \u043f\u0440\u0438 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u0442\u0435\u043a\u0441\u0442\u043e\u0432 \u0441\u0441\u044b\u043b\u0430\u0435\u0442\u0441\u044f \u043d\u0435 \u043d\u0430 \u043f\u0435\u0440\u0432\u043e\u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438, \u0430 \u043d\u0430 \u043a\u043e\u043f\u0438\u0438, \u0443\u0441\u0438\u043b\u0438\u0432\u0430\u044f \u0434\u0435\u0437\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u044b\u0439 \u0448\u0443\u043c.<\/li>\n<\/ol>\n<div><\/div>\n<strong>\u0427\u0442\u043e \u0443\u043c\u0435\u044e\u0442 LLM \u0432 \u0431\u043e\u0440\u044c\u0431\u0435 \u0441 LLM Grooming?<\/strong>\n<strong>\u0424\u0438\u043b\u044c\u0442\u0440\u0430\u0446\u0438\u044f \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u0440\u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0438<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0411\u043e\u043b\u044c\u0448\u0438\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 \u0432\u0440\u043e\u0434\u0435 GPT \u043e\u0431\u0443\u0447\u0430\u044e\u0442\u0441\u044f \u043d\u0430 \u043e\u0442\u043e\u0431\u0440\u0430\u043d\u043d\u044b\u0445, \u043e\u0447\u0438\u0449\u0435\u043d\u043d\u044b\u0445 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u0430\u0445. \u0412\u043e \u0432\u0440\u0435\u043c\u044f \u043f\u043e\u0434\u0433\u043e\u0442\u043e\u0432\u043a\u0438 \u0434\u0430\u043d\u043d\u044b\u0445 \u043f\u0440\u0438\u043c\u0435\u043d\u044f\u044e\u0442\u0441\u044f \u0444\u0438\u043b\u044c\u0442\u0440\u044b, \u0443\u0434\u0430\u043b\u044f\u044e\u0449\u0438\u0435:\n<ul data-spread=\"false\">\n \t<li>\u0441\u043f\u0430\u043c,<\/li>\n \t<li>\u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0443\u044e \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u044e,<\/li>\n \t<li>SEO-\u0444\u0435\u0440\u043c\u044b,<\/li>\n \t<li>\u0442\u043e\u043a\u0441\u0438\u0447\u043d\u044b\u0439 \u0438\u043b\u0438 \u043c\u0430\u043d\u0438\u043f\u0443\u043b\u044f\u0442\u0438\u0432\u043d\u044b\u0439 \u043a\u043e\u043d\u0442\u0435\u043d\u0442.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u042d\u0442\u043e \u043f\u0435\u0440\u0432\u0430\u044f \u043b\u0438\u043d\u0438\u044f \u0437\u0430\u0449\u0438\u0442\u044b \u043e\u0442 LLM grooming \u2014 \u043d\u0435 \u0434\u0430\u0442\u044c \u0432\u0440\u0435\u0434\u043d\u044b\u043c \u0434\u0430\u043d\u043d\u044b\u043c \u043f\u043e\u043f\u0430\u0441\u0442\u044c \u0432 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435.<\/li>\n<\/ul>\n<strong>\u041a\u043e\u043d\u0442\u0440\u043e\u043b\u044c \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u041c\u043e\u0434\u0435\u043b\u0438 \u043f\u0440\u043e\u0445\u043e\u0434\u044f\u0442 \u0442\u043e\u043d\u043a\u0443\u044e \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0443 (fine-tuning) \u0438 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435 \u0441 \u0443\u0447\u0430\u0441\u0442\u0438\u0435\u043c \u043b\u044e\u0434\u0435\u0439 (RLHF), \u0447\u0442\u043e\u0431\u044b \u043d\u0435 \u043f\u043e\u0432\u0442\u043e\u0440\u044f\u0442\u044c \u043b\u043e\u0436\u043d\u044b\u0435 \u0438\u043b\u0438 \u0432\u0440\u0435\u0434\u043d\u044b\u0435 \u043d\u0430\u0440\u0440\u0430\u0442\u0438\u0432\u044b, \u0434\u0430\u0436\u0435 \u0435\u0441\u043b\u0438 \u043e\u043d\u0438 \u0435\u0441\u0442\u044c \u0432 \u0434\u0430\u043d\u043d\u044b\u0445.<\/li>\n \t<li>\u041d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u0434\u0430\u0436\u0435 \u0435\u0441\u043b\u0438 \u043a\u0442\u043e-\u0442\u043e \u043c\u0430\u0441\u0441\u043e\u0432\u043e \u043f\u0443\u0431\u043b\u0438\u043a\u0443\u0435\u0442 \u0434\u0435\u0437\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u043e \u0432\u0430\u043a\u0446\u0438\u043d\u0430\u0445 \u2014 \u044d\u0442\u043e \u043d\u0435 \u0433\u0430\u0440\u0430\u043d\u0442\u0438\u0440\u0443\u0435\u0442, \u0447\u0442\u043e \u043c\u043e\u0434\u0435\u043b\u044c \u0431\u0443\u0434\u0435\u0442 \u0435\u0451 \u0432\u043e\u0441\u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u044c.<\/li>\n<\/ul>\n<strong>\u0424\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433 \u0438 \u043c\u0435\u0442\u0430-\u043f\u043e\u043d\u0438\u043c\u0430\u043d\u0438\u0435<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u042f \u043c\u043e\u0433\u0443 \u043f\u0440\u043e\u0432\u0435\u0440\u0438\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u043d\u0430\u0439\u0442\u0438 \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0438, \u0441\u043e\u043f\u043e\u0441\u0442\u0430\u0432\u0438\u0442\u044c \u0444\u0430\u043a\u0442\u044b, \u0438, \u0435\u0441\u043b\u0438 \u043d\u0430\u0434\u043e, \u0443\u043a\u0430\u0437\u0430\u0442\u044c, \u0447\u0442\u043e \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u0435 \u0441\u043f\u043e\u0440\u043d\u043e\u0435 \u0438\u043b\u0438 \u043b\u043e\u0436\u043d\u043e\u0435.<\/li>\n<\/ul>\n\u2757 \u041d\u043e \u0435\u0441\u0442\u044c \u0438 \u043e\u0433\u0440\u0430\u043d\u0438\u0447\u0435\u043d\u0438\u044f:\n<ul data-spread=\"false\">\n \t<li>\u0415\u0441\u043b\u0438 LLM grooming \u043d\u0435\u0437\u0430\u043c\u0435\u0442\u0435\u043d \u0438 \u0442\u043e\u043d\u043a\u0438\u0439 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, \u043c\u0430\u0441\u0441\u043e\u0432\u043e\u0435, \u043d\u043e \u043f\u0440\u0430\u0432\u0434\u043e\u043f\u043e\u0434\u043e\u0431\u043d\u043e\u0435 \u043f\u0435\u0440\u0435\u043f\u0438\u0441\u044b\u0432\u0430\u043d\u0438\u0435 \u0438\u0441\u0442\u043e\u0440\u0438\u0438), \u0435\u0433\u043e \u0442\u0440\u0443\u0434\u043d\u0435\u0435 \u043e\u0442\u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432\u0430\u0442\u044c.<\/li>\n \t<li>\u041e\u0442\u043a\u0440\u044b\u0442\u044b\u0435 \u043c\u043e\u0434\u0435\u043b\u0438 (\u0442\u0438\u043f\u0430 LLaMA, Mistral \u0438 \u0434\u0440.), \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u043e\u0431\u0443\u0447\u0430\u044e\u0442\u0441\u044f \"\u043d\u0430 \u0447\u0451\u043c \u043f\u043e\u043f\u0430\u043b\u043e\", \u043c\u043e\u0433\u0443\u0442 \u0441\u0438\u043b\u044c\u043d\u0435\u0435 \u043f\u043e\u0441\u0442\u0440\u0430\u0434\u0430\u0442\u044c \u043e\u0442 LLM grooming.<\/li>\n \t<li>\u0411\u043e\u0440\u044c\u0431\u0430 \u0441 \u044d\u0442\u0438\u043c \u2014 \u043d\u0435 \u0437\u0430\u0434\u0430\u0447\u0430 \u043c\u043e\u0434\u0435\u043b\u0438, \u0430 \u0441\u043a\u043e\u0440\u0435\u0435 \u0437\u0430\u0434\u0430\u0447\u0430 \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432, \u044d\u0442\u0438\u043a\u043e\u0432, \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u043e\u0432 \u0438 \u0434\u0430\u0442\u0430\u0441\u0435\u0442-\u0438\u043d\u0436\u0435\u043d\u0435\u0440\u043e\u0432.<\/li>\n<\/ul>\n\ud83e\udd16 \u0427\u0442\u043e \u0442\u044b \u043c\u043e\u0436\u0435\u0448\u044c \u0434\u0435\u043b\u0430\u0442\u044c \u043a\u0430\u043a \u0447\u0435\u043b\u043e\u0432\u0435\u043a:\n<ul data-spread=\"false\">\n \t<li>\u0421\u043e\u0437\u0434\u0430\u0432\u0430\u0442\u044c \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u043a\u043e\u043d\u0442\u0435\u043d\u0442, \u0447\u0442\u043e\u0431\u044b \u043e\u043d \u043f\u043e\u043f\u0430\u0434\u0430\u043b \u0432 \u0434\u0430\u0442\u0430\u0441\u0435\u0442\u044b.<\/li>\n \t<li>\u041f\u0440\u043e\u0432\u043e\u0434\u0438\u0442\u044c \u0430\u0443\u0434\u0438\u0442 \u0418\u0418, \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u044f, \u043a\u0430\u043a \u043e\u043d \u0440\u0435\u0430\u0433\u0438\u0440\u0443\u0435\u0442 \u043d\u0430 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0437\u0430\u0441\u0435\u044f\u043d\u043d\u044b\u0435 \u0442\u0435\u043c\u044b.<\/li>\n \t<li>\u041f\u0440\u0438\u043c\u0435\u043d\u044f\u0442\u044c \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u043e\u0442\u0441\u043b\u0435\u0436\u0438\u0432\u0430\u043d\u0438\u044f \"\u0432\u0431\u0440\u043e\u0441\u043e\u0432\", \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0435\u0441\u043b\u0438 \u0437\u0430\u043d\u0438\u043c\u0430\u0435\u0448\u044c\u0441\u044f OSINT, \u043c\u0435\u0434\u0438\u0430\u0433\u0440\u0430\u043c\u043e\u0442\u043d\u043e\u0441\u0442\u044c\u044e \u0438\u043b\u0438 \u0444\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433\u043e\u043c.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u00ab\u0441\u043b\u043e\u0438 \u0444\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433\u0430\u00bb \u0434\u043b\u044f \u0418\u0418?<\/strong>\n\u042d\u0442\u043e \u043c\u043e\u0434\u0443\u043b\u0438, \u043c\u043e\u0434\u0435\u043b\u0438 \u0438\u043b\u0438 API, \u043a\u043e\u0442\u043e\u0440\u044b\u0435:\n<ul data-spread=\"false\">\n \t<li>\u043f\u0440\u043e\u0432\u0435\u0440\u044f\u044e\u0442 \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u043d\u0430 \u0434\u043e\u0441\u0442\u043e\u0432\u0435\u0440\u043d\u043e\u0441\u0442\u044c;<\/li>\n \t<li>\u0443\u043a\u0430\u0437\u044b\u0432\u0430\u044e\u0442, \u043d\u0443\u0436\u043d\u043e \u043b\u0438 \u0443\u0442\u043e\u0447\u043d\u0435\u043d\u0438\u0435;<\/li>\n \t<li>\u043b\u0438\u0431\u043e \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u044e\u0442 \u0443\u0440\u043e\u0432\u0435\u043d\u044c \u043f\u0440\u0430\u0432\u0434\u043e\u043f\u043e\u0434\u043e\u0431\u043d\u043e\u0441\u0442\u0438 \u0444\u0440\u0430\u0437\u044b.<\/li>\n<\/ul>\n\u0422\u0430\u043a\u0438\u0435 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u0440\u0430\u0431\u043e\u0442\u0430\u044e\u0442 \u0432 \u0441\u0432\u044f\u0437\u043a\u0435 \u0441 LLM, \u0447\u0442\u043e\u0431\u044b:\n<ul data-spread=\"false\">\n \t<li>\u043c\u0438\u043d\u0438\u043c\u0438\u0437\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u0440\u0430\u0441\u043f\u0440\u043e\u0441\u0442\u0440\u0430\u043d\u0435\u043d\u0438\u0435 \u0434\u0435\u0437\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438;<\/li>\n \t<li>\u0444\u0438\u043b\u044c\u0442\u0440\u043e\u0432\u0430\u0442\u044c \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0435;<\/li>\n \t<li>\u043f\u043e\u0432\u044b\u0441\u0438\u0442\u044c \u0434\u043e\u0432\u0435\u0440\u0438\u0435 \u043a \u043e\u0442\u0432\u0435\u0442\u0430\u043c \u043c\u043e\u0434\u0435\u043b\u0438.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f \u041f\u0440\u0438\u043c\u0435\u0440\u044b\n\n\ud83d\udd0e <strong>ClaimBuster<\/strong> \u0421\u0443\u0442\u044c: \u0430\u043b\u0433\u043e\u0440\u0438\u0442\u043c, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0430\u0432\u0442\u043e\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438 \u043d\u0430\u0445\u043e\u0434\u0438\u0442 \u0444\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433\u043e\u0432\u043e \u0437\u043d\u0430\u0447\u0438\u043c\u044b\u0435 \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f \u0432 \u0442\u0435\u043a\u0441\u0442\u0435.\n\n\ud83d\udccc \u0413\u0434\u0435 \u043f\u043e\u043b\u0435\u0437\u0435\u043d:\n<ul data-spread=\"false\">\n \t<li>\u0434\u043b\u044f \u0441\u043a\u0430\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u043d\u043e\u0432\u043e\u0441\u0442\u0435\u0439, \u043f\u043e\u0441\u0442\u043e\u0432, \u0440\u0435\u0447\u0435\u0439 \u043f\u043e\u043b\u0438\u0442\u0438\u043a\u043e\u0432;<\/li>\n \t<li>\u0434\u043b\u044f \u0441\u043e\u0437\u0434\u0430\u043d\u0438\u044f \u0434\u0430\u0442\u0430\u0444\u0440\u0435\u0439\u043c\u0430 \u0441 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0444\u0435\u0439\u043a\u043e\u0432\u044b\u043c\u0438\/\u0432\u0432\u043e\u0434\u044f\u0449\u0438\u043c\u0438 \u0432 \u0437\u0430\u0431\u043b\u0443\u0436\u0434\u0435\u043d\u0438\u0435 \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u044f\u043c\u0438;<\/li>\n \t<li>\u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043a\u0430\u043a \u0444\u0438\u043b\u044c\u0442\u0440 \u043f\u0435\u0440\u0435\u0434 \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u0435\u043c \u043c\u043e\u0434\u0435\u043b\u0438.<\/li>\n<\/ul>\n\ud83e\uddea \u041a\u0430\u043a \u0440\u0430\u0431\u043e\u0442\u0430\u0435\u0442:\n<ul data-spread=\"false\">\n \t<li>\u041f\u0440\u0438\u043d\u0438\u043c\u0430\u0435\u0442 \u043d\u0430 \u0432\u0445\u043e\u0434 \u0442\u0435\u043a\u0441\u0442 (\u0438\u043b\u0438 \u0442\u0440\u0430\u043d\u0441\u043a\u0440\u0438\u043f\u0446\u0438\u044e \u0440\u0435\u0447\u0438).<\/li>\n \t<li>\u0412\u044b\u0434\u0430\u0451\u0442: \u0444\u0440\u0430\u0437\u0430 \u044d\u0442\u043e \"check-worthy\" (\u043d\u0443\u0436\u0434\u0430\u044e\u0449\u0430\u044f\u0441\u044f \u0432 \u043f\u0440\u043e\u0432\u0435\u0440\u043a\u0435) \u0438\u043b\u0438 \u043d\u0435\u0442.<\/li>\n<\/ul>\n\ud83d\udcce \u0418\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u0442\u0441\u044f \u0432: FactStream \u043e\u0442 Duke University.\n\n\ud83d\udcda <strong>TrustworthyQA<\/strong> \u0421\u0443\u0442\u044c: \u0434\u0430\u0442\u0430\u0441\u0435\u0442 \u0438 \u043c\u043e\u0434\u0435\u043b\u044c, \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0430\u043d\u043d\u044b\u0435 \u0434\u043b\u044f \u043e\u0446\u0435\u043d\u043a\u0438 \u043d\u0430\u0434\u0451\u0436\u043d\u043e\u0441\u0442\u0438 \u0443\u0442\u0432\u0435\u0440\u0436\u0434\u0435\u043d\u0438\u0439, \u0441\u0434\u0435\u043b\u0430\u043d\u043d\u044b\u0445 \u0432 \u043e\u0442\u0432\u0435\u0442\u0430\u0445 LLM.\n\n\ud83d\udccc \u0413\u0434\u0435 \u043f\u043e\u043b\u0435\u0437\u0435\u043d:\n<ul data-spread=\"false\">\n \t<li>\u043a\u0430\u043a \u0434\u043e\u043f\u043e\u043b\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0439 \u0441\u043b\u043e\u0439 \u0432 pipeline \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u0438\u0438 \u0442\u0435\u043a\u0441\u0442\u0430;<\/li>\n \t<li>\u0434\u043b\u044f \u0442\u0440\u0435\u043d\u0438\u0440\u043e\u0432\u043a\u0438 \u043c\u043e\u0434\u0435\u043b\u0435\u0439 \u043d\u0430 \u00ab\u043f\u043e\u0434\u043e\u0437\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435\u00bb \u0437\u0430\u043f\u0440\u043e\u0441\u044b (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440: \"\u0411\u0438\u043b\u043b \u0413\u0435\u0439\u0442\u0441 \u0443\u043f\u0440\u0430\u0432\u043b\u044f\u0435\u0442 \u043f\u043e\u0433\u043e\u0434\u043e\u0439?\").<\/li>\n<\/ul>\n\ud83e\udde0 \u0427\u0435\u043c \u0438\u043d\u0442\u0435\u0440\u0435\u0441\u0435\u043d:\n<ul data-spread=\"false\">\n \t<li>\u041e\u043d \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u043e \u043f\u0440\u043e\u0432\u0435\u0440\u044f\u0435\u0442 \u0444\u0430\u043a\u0442\u044b, \u0430 \u043e\u0446\u0435\u043d\u0438\u0432\u0430\u0435\u0442 \u043d\u0430\u0434\u0451\u0436\u043d\u043e\u0441\u0442\u044c \u043e\u0442\u0432\u0435\u0442\u0430 \u0418\u0418 \u043d\u0430 \u043f\u043e\u0442\u0435\u043d\u0446\u0438\u0430\u043b\u044c\u043d\u043e \u0441\u043e\u043c\u043d\u0438\u0442\u0435\u043b\u044c\u043d\u044b\u0435 \u0432\u043e\u043f\u0440\u043e\u0441\u044b.<\/li>\n \t<li>\u041c\u043e\u0434\u0435\u043b\u044c \u0443\u0447\u0438\u0442\u0441\u044f \u0433\u043e\u0432\u043e\u0440\u0438\u0442\u044c \u00ab\u043d\u0435 \u0437\u043d\u0430\u044e\u00bb \u0438\u043b\u0438 \u0443\u043a\u0430\u0437\u044b\u0432\u0430\u0442\u044c \u043d\u0430 \u0441\u043f\u043e\u0440\u043d\u043e\u0441\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u0420\u0435\u043a\u043e\u043c\u0435\u043d\u0434\u0430\u0446\u0438\u0438 \u0434\u043b\u044f \u0440\u0430\u0437\u043d\u044b\u0445 \u0446\u0435\u043b\u0435\u0432\u044b\u0445 \u0433\u0440\u0443\u043f\u043f<\/strong>\n<strong>\u0418\u0437\u0443\u0447\u0430\u044e\u0449\u0438\u0435 \u0438\u043d\u0444\u043e\u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0438<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0412\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u043a \u0438\u0441\u0442\u043e\u0447\u043d\u0438\u043a\u0430\u043c, \u0441\u043e\u0437\u0434\u0430\u043d\u043d\u044b\u043c \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0435\u0439, \u0430 \u0434\u043b\u044f \u043c\u0430\u0448\u0438\u043d.<\/li>\n \t<li>\u041c\u043e\u043d\u0438\u0442\u043e\u0440\u0438\u043d\u0433 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0445 \u0441\u0435\u0442\u0435\u0439 \u0441 \u043f\u043e\u0434\u043e\u0437\u0440\u0438\u0442\u0435\u043b\u044c\u043d\u043e \u043e\u0434\u043d\u043e\u043e\u0431\u0440\u0430\u0437\u043d\u044b\u043c \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u043e\u043c.<\/li>\n<\/ul>\n<strong>\u0420\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u0438 LLM<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0423\u0434\u0435\u043b\u044f\u0442\u044c \u0432\u043d\u0438\u043c\u0430\u043d\u0438\u0435 \u043f\u0440\u043e\u0438\u0441\u0445\u043e\u0436\u0434\u0435\u043d\u0438\u044e \u043e\u0431\u0443\u0447\u0430\u044e\u0449\u0438\u0445 \u0434\u0430\u043d\u043d\u044b\u0445.<\/li>\n \t<li>\u0412\u0441\u0442\u0440\u0430\u0438\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0443\u043b\u0438 \u0444\u0430\u043a\u0442\u0447\u0435\u043a\u0438\u043d\u0433\u0430 \u0438 \u0441\u0438\u0441\u0442\u0435\u043c\u044b \u043e\u0446\u0435\u043d\u043a\u0438 \u0434\u043e\u0441\u0442\u043e\u0432\u0435\u0440\u043d\u043e\u0441\u0442\u0438 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, TrustworthyQA).<\/li>\n \t<li>\u0418\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u0444\u0438\u043b\u044c\u0442\u0440\u044b \u0442\u0438\u043f\u0430 ClaimBuster (\u0438\u043b\u0438 \u0438\u0445 \u0430\u043d\u0430\u043b\u043e\u0433\u0438 \u0434\u043b\u044f \u043a\u0438\u0440\u0438\u043b\u043b\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u044f\u0437\u044b\u043a\u043e\u0432) \u043d\u0430 \u0441\u0442\u0430\u0434\u0438\u0438 \u043f\u0440\u0435\u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0438\u043d\u0433\u0430 \u0434\u0430\u043d\u043d\u044b\u0445.<\/li>\n<\/ul>\n<strong>\u0424\u0430\u043a\u0442\u0447\u0435\u043a\u0435\u0440\u044b \u0438 \u0436\u0443\u0440\u043d\u0430\u043b\u0438\u0441\u0442\u044b<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0412\u044b\u044f\u0432\u043b\u044f\u0442\u044c \u043c\u043d\u043e\u0433\u043e\u043a\u0440\u0430\u0442\u043d\u0443\u044e \u043f\u0443\u0431\u043b\u0438\u043a\u0430\u0446\u0438\u044e \u043e\u0434\u043d\u0438\u0445 \u0438 \u0442\u0435\u0445 \u0436\u0435 \u0442\u0435\u043a\u0441\u0442\u043e\u0432 \u043f\u043e\u0434 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0434\u043e\u043c\u0435\u043d\u0430\u043c\u0438.<\/li>\n \t<li>\u0421\u043e\u043f\u043e\u0441\u0442\u0430\u0432\u043b\u044f\u0442\u044c, \u043e\u0442\u043a\u0443\u0434\u0430 LLM \u0447\u0435\u0440\u043f\u0430\u0435\u0442 \u043f\u0440\u0438\u043c\u0435\u0440\u044b \u0438 \u0446\u0438\u0442\u0430\u0442\u044b.<\/li>\n \t<li>\u041f\u0440\u0438\u043c\u0435\u043d\u044f\u0442\u044c \u043f\u0430\u0440\u0441\u0435\u0440\u044b \u0438 \u0438\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u0430\u043d\u0430\u043b\u0438\u0437\u0430 \u0441\u0435\u0442\u0435\u0432\u044b\u0445 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 (\u043d\u0430\u043f\u0440\u0438\u043c\u0435\u0440, trafilatura, Graphistry).<\/li>\n<\/ul>\n<div><\/div>\n\u042f\u0432\u043b\u0435\u043d\u0438\u0435 LLM grooming \u2014 \u044d\u0442\u043e \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u043e \u043d\u043e\u0432\u044b\u0439 \u0444\u0440\u043e\u043d\u0442 \u0434\u0435\u0437\u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u0438, \u043d\u043e \u0438 \u0432\u044b\u0437\u043e\u0432 \u0434\u043b\u044f \u0440\u0430\u0437\u0440\u0430\u0431\u043e\u0442\u0447\u0438\u043a\u043e\u0432 \u0438 \u0440\u0435\u0433\u0443\u043b\u044f\u0442\u043e\u0440\u043e\u0432. \u0421\u043e\u0437\u0434\u0430\u044e\u0442\u0441\u044f \u043a\u043e\u043d\u0442\u0435\u043d\u0442\u043d\u044b\u0435 \u0444\u0435\u0440\u043c\u044b, \u043a\u043e\u0442\u043e\u0440\u044b\u0435 \u0432\u043e\u0437\u0434\u0435\u0439\u0441\u0442\u0432\u0443\u044e\u0442 \u043d\u0435 \u043d\u0430 \u0430\u0443\u0434\u0438\u0442\u043e\u0440\u0438\u044e, \u0430 \u043d\u0430 \u043c\u0430\u0448\u0438\u043d\u044b. \u0411\u043e\u0440\u044c\u0431\u0430 \u0441 \u044d\u0442\u0438\u043c\u0438 \u043f\u0440\u043e\u0446\u0435\u0441\u0441\u0430\u043c\u0438 \u0442\u0440\u0435\u0431\u0443\u0435\u0442 \u043d\u043e\u0432\u044b\u0445 \u043f\u043e\u0434\u0445\u043e\u0434\u043e\u0432 \u043a \u0430\u0443\u0434\u0438\u0442\u0443 \u0434\u0430\u043d\u043d\u044b\u0445, \u0438\u043d\u0434\u0435\u043a\u0441\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044e \u0438 \u0442\u0440\u0435\u043d\u0434\u0430\u043c \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f LLM.\n\n<em>\u0427\u0435\u043c \u0440\u0430\u043d\u044c\u0448\u0435 \u043c\u044b \u043d\u0430\u0443\u0447\u0438\u043c\u0441\u044f \u0440\u0430\u0441\u043f\u043e\u0437\u043d\u0430\u0432\u0430\u0442\u044c LLM Grooming, \u0442\u0435\u043c \u043b\u0443\u0447\u0448\u0435 \u0441\u043c\u043e\u0436\u0435\u043c \u0437\u0430\u0449\u0438\u0442\u0438\u0442\u044c \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u043e\u043d\u043d\u0443\u044e \u0441\u0440\u0435\u0434\u0443 \u0431\u0443\u0434\u0443\u0449\u0435\u0433\u043e.<\/em>","_ru_post_name":"llm-grooming","_ru_post_excerpt":"","_ru_post_title":"LLM Grooming \u043a\u0430\u043a \u043d\u043e\u0432\u0430\u044f \u0443\u0433\u0440\u043e\u0437\u0430: \u043a\u0430\u043a \u043f\u0440\u043e\u043a\u0440\u0435\u043c\u043b\u0451\u0432\u0441\u043a\u0438\u0435 \u0441\u0435\u0442\u0438 \u0433\u043e\u0442\u043e\u0432\u044f\u0442 \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u0434\u043b\u044f \u0418\u0418","_eng_post_content":"What is LLM Grooming - manipulation of AI training environment or substitution of AI training data.\nImagine that someone is massively seeding the internet with articles, blogs, fake news, specifically written not for you to read, but for AI.\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models think.\nThis is LLM grooming \u2014 information programming of artificial intelligence through \"prepared\" data.\nLLM grooming is a strategy where malicious actors massively publish false or manipulative information on the internet, intended not for people, but for automatic data collection systems used in training large language models (LLMs).\nThis is not for clicks and likes, but to influence how future generations of ChatGPT, Claude, Gemini and other models \"think\".\nThe goal is to embed certain narratives into the models so they reproduce them in their responses. This can lead to distortion of representations of historical events, politics, geography, healthcare, etc.\nLLM Grooming is an attack not on human consciousness, but on the knowledge architecture that language models build.\n<strong>How it looks in practice<\/strong>\n<ul data-spread=\"false\">\n \t<li>Articles, blogs, fake news are massively published on the internet, written in a way to be uninteresting to users but suitable for data collection algorithms.<\/li>\n \t<li>The same text is published as if from independent sources, creating an illusion of consistency.<\/li>\n \t<li>The main feature: poor UX, absence of comments, excessive repetitiveness, automatic translations, SEO structure \u2014 everything to \"appeal\" to parsers and bots.<\/li>\n<\/ul>\n\ud83e\udde8 How might this look?\n<ul data-spread=\"false\">\n \t<li>At first glance \u2014 an ordinary blog or forum, but all posts look too uniform, repeating the same version of events.<\/li>\n \t<li>Someone creates hundreds of articles about \"scientists who proved the harm of electricity\" \u2014 not for fake arguments in comments, but so they end up in LLM training datasets.<\/li>\n \t<li>\"Evidence\" appears on TikTok that \"Poland was part of Russia\" \u2014 again, not for views, but for indexing.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f How to identify LLM Grooming? (methods of conscious auditing)\n<ol start=\"1\" data-spread=\"true\">\n \t<li>Listen to what models say\n<ul data-spread=\"false\">\n \t<li>Ask sensitive questions and see if the answer leans in one direction, especially if it doesn't correspond to the balance in the real expert community.<\/li>\n \t<li>Compare the behavior of models from different developers: GPT, Claude, Gemini. If they all \"fall\" for one narrative \u2014 it may have passed through datasets.<\/li>\n<\/ul>\n<\/li>\n \t<li>Monitor sources\n<ul data-spread=\"false\">\n \t<li>If a model references marginal or uniform sites \u2014 that's a warning bell.<\/li>\n \t<li>You can use an analysis layer (for example, connect trafilatura, newspaper3k libraries) and see which domains come up most often.<\/li>\n<\/ul>\n<\/li>\n \t<li>Look at noise in the information space\n<ul data-spread=\"false\">\n \t<li>If there's suddenly a surge of similar texts, structures, or terms \u2014 this could be an attempt at a massive injection into the search system and, through it, into future LLM training.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\ud83e\uddf7 How to protect yourself? (if you're a researcher, developer, or just concerned) \ud83d\udd12 If you work with AI:\n<ul data-spread=\"false\">\n \t<li>Monitor meta-content: what data gets into your models? Use filters, check domains, validate sources.<\/li>\n \t<li>Add fact-checking layers: filters like ClaimBuster, models like TrustworthyQA.<\/li>\n<\/ul>\n\ud83d\udd75\ufe0f If you're a journalist, activist, or analyst:\n<ul data-spread=\"false\">\n \t<li>Implement monitoring of key topics on Google, TikTok, Telegram, YouTube.<\/li>\n \t<li>Look for repeating key phrases, phrasing patterns \u2014 this could be a signal of \"info-seeding.\"<\/li>\n<\/ul>\n\ud83d\udcac If you're just a user:\n<ul data-spread=\"false\">\n \t<li>Be skeptical. Even if AI claims something \u2014 verify it.<\/li>\n \t<li>Remember: AI might \"repeat\" those who are louder, not those who are right.<\/li>\n<\/ul>\n\ud83d\udccc What's next?\n<ul data-spread=\"false\">\n \t<li>LLM grooming is a new vector of FIMI (foreign information manipulation and interference). It's inconspicuous, quiet, working for the long game.<\/li>\n \t<li>That's why it's important not only to protect AI from fakes but also to teach it to think critically \u2014 just like people.<\/li>\n<\/ul>\n<div><\/div>\n<strong>Pravda Network Case: networks not for people<\/strong>\nThe <a href=\"https:\/\/static1.squarespace.com\/static\/6612cbdfd9a9ce56ef931004\/t\/67fd396818196f3d1666bc23\/1744648558879\/PK+Report.pdf\">report<\/a> by the American Sunlight Project (February 2025) describes the activities of the Pravda Network \u2014 part of a broader structure called \"Portal Kombat\". This structure includes domains and subdomains publishing identical texts with pro-Russian narratives, in different languages and under different brands.\nThese sites:\n<ul data-spread=\"false\">\n \t<li>are designed as news sources,<\/li>\n \t<li>have limited functionality for humans (poor navigation, inconvenient design),<\/li>\n \t<li>are most likely intended for indexing by artificial models.<\/li>\n<\/ul>\nAccording to the dashboard at <a href=\"https:\/\/portal-kombat.com\/\">portal-kombat.com<\/a>, the network includes 182 sites combining the same texts with different language metadata.\n<img class=\"alignnone size-full wp-image-3344\" src=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png\" alt=\"\" width=\"2560\" height=\"1311\" \/>\nThe dashboard displays a list of domains, their registration date, registering country, and communication \"sphere\" (e.g., national affiliation of the site). This is an interactive tool allowing researchers to track the structure of the network, scale of coverage, and distribution of key narratives.\n<div><\/div>\n<strong>Network Motives<\/strong>\nPrevious publications on potential motives of the \"Pravda\" network focused on its anti-Ukrainian and pro-war nature, as well as possible implications for European elections in 2024. However, as this network continues to grow and change, a more thorough examination is needed to determine the possible trajectory of its development. ASP considers three possible, non-mutually exclusive motives for creating the network, which focus on its technological features and shortcomings. These motives are not tied to specific countries, regions, or political events, as the goals of pro-Russian information operations may change.\n<strong>Explanation A: LLM training<\/strong>\nThe most significant result of ASP's research was not the expansion of the network or its orientation toward non-Western states, but the model of future information operations built on automation. The \"Pravda\" network \u2014 huge, fast-growing, user-unfriendly \u2014 is most likely designed for automatic agents: web crawlers, scrapers, and algorithms that form LLMs. This is mass production and duplication of content to get into future AI datasets.\nASP calls this tactic <strong>LLM grooming<\/strong> \u2014 deliberate saturation of the internet with information intended for machine consumption. In June 2024, NewsGuard showed that leading LLMs reproduce Russian disinformation in 31.8% of cases on average. If measures are not taken, LLM grooming poses a threat to the integrity of the open internet.\nFebruary 2023 \u2014 the creation date of the \"Pravda\" network \u2014 coincides with the moment of popularization of generative AI. Attempts to attract crawlers through SEO optimization have been recorded before. Unlike traditional SEO, the goal of LLM grooming is not just to increase visibility, but to <strong>program AI<\/strong> to repeat certain narratives. This is still a little-studied threat.\n<strong>Explanation B: mass saturation<\/strong>\nThe network publishes a huge amount of material daily, saturating the internet with pro-Russian content. This increases:\n<ul data-spread=\"false\">\n \t<li>the likelihood that a user will come across the desired narrative,<\/li>\n \t<li>the chance that external sources (e.g., Wikipedia) will reference these materials.<\/li>\n<\/ul>\nThe mass impact mechanism forms the <strong>illusion of truth effect<\/strong>: the more often a person encounters a statement, the higher the probability they will believe it.\n<strong>Explanation C: effect of illusory truth from multiple sources<\/strong>\nThe network distributes the same content through multiple channels: websites, Telegram, X, VK, and even Bluesky. This creates the illusion of confirmed information from \"different\" sources. Both deliberate \"laundering\" of information (e.g., when other pro-Russian resources reference the network) and unintentional (when a respected organization or person shares a link without knowing its origin) come into play.\nAll three motives reinforce each other. The more pages, URLs, and translations the network creates, the higher the probability that narratives will be accepted by both humans and machines. Although the quality of the sites is low, this doesn't prevent them from becoming part of the digital footprint considered by LLMs.\n<div>\n<iframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/kk7tBTg_Xbo?si=GANrkylWs1tFMyKi\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<strong>LLM-grooming Scenarios<\/strong>\nThe report authors identify three key goals of such networks:\n<ol start=\"1\" data-spread=\"false\">\n \t<li><strong>Inclusion in datasets<\/strong> \u2014 sites are indexed in search engines and included in LLM training, embedding pro-Kremlin narratives into the model's architecture.<\/li>\n \t<li><strong>Creating an illusion of independent sources<\/strong> \u2014 the same text is placed on hundreds of sites, creating an effect of \"consensus\".<\/li>\n \t<li><strong>Blurring the information field<\/strong> \u2014 when generating texts, LLMs reference not original sources but copies, amplifying disinformation noise.<\/li>\n<\/ol>\n<div><\/div>\n<strong>What can LLMs do to combat LLM Grooming?<\/strong>\n<strong>Data filtering during training<\/strong>\n<ul data-spread=\"false\">\n \t<li>Large models like GPT are trained on selected, cleaned datasets. During data preparation, filters are applied that remove:\n<ul data-spread=\"false\">\n \t<li>spam,<\/li>\n \t<li>automated generation,<\/li>\n \t<li>SEO farms,<\/li>\n \t<li>toxic or manipulative content.<\/li>\n<\/ul>\n<\/li>\n \t<li>This is the first line of defense against LLM grooming \u2014 preventing harmful data from entering the training.<\/li>\n<\/ul>\n<strong>Generation quality control<\/strong>\n<ul data-spread=\"false\">\n \t<li>Models undergo fine-tuning and Reinforcement Learning from Human Feedback (RLHF) to avoid repeating false or harmful narratives, even if they exist in the data.<\/li>\n \t<li>For example, even if someone massively publishes disinformation about vaccines \u2014 this doesn't guarantee the model will reproduce it.<\/li>\n<\/ul>\n<strong>Fact-checking and meta-understanding<\/strong>\n<ul data-spread=\"false\">\n \t<li>I can verify information, find sources, compare facts, and, if necessary, indicate that a statement is controversial or false.<\/li>\n<\/ul>\n\u2757 But there are limitations:\n<ul data-spread=\"false\">\n \t<li>If LLM grooming is subtle and hard to notice (e.g., massive but plausible rewriting of history), it's harder to filter.<\/li>\n \t<li>Open models (like LLaMA, Mistral, etc.) that train on \"whatever they can find\" may suffer more from LLM grooming.<\/li>\n \t<li>Fighting this is not the task of the model, but rather the task of developers, ethicists, auditors, and dataset engineers.<\/li>\n<\/ul>\n\ud83e\udd16 What you can do as a human:\n<ul data-spread=\"false\">\n \t<li>Create quality content so it gets into datasets.<\/li>\n \t<li>Conduct AI audits, checking how it responds to potentially seeded topics.<\/li>\n \t<li>Apply tools to track \"injections,\" especially if you're involved in OSINT, media literacy, or fact-checking.<\/li>\n<\/ul>\n<div><\/div>\n<strong>What are \"fact-checking layers\" for AI?<\/strong>\nThese are modules, models, or APIs that:\n<ul data-spread=\"false\">\n \t<li>check statements for accuracy;<\/li>\n \t<li>indicate if clarification is needed;<\/li>\n \t<li>or assess the level of plausibility of a phrase.<\/li>\n<\/ul>\nSuch tools work in conjunction with LLMs to:\n<ul data-spread=\"false\">\n \t<li>minimize the spread of disinformation;<\/li>\n \t<li>filter training data;<\/li>\n \t<li>increase trust in model responses.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f Examples\n\ud83d\udd0e <strong>ClaimBuster<\/strong> Essence: an algorithm that automatically finds fact-check-worthy claims in text.\n\ud83d\udccc Where it's useful:\n<ul data-spread=\"false\">\n \t<li>for scanning news, posts, politicians' speeches;<\/li>\n \t<li>for creating a dataframe with potentially fake\/misleading statements;<\/li>\n \t<li>can be used as a filter before training a model.<\/li>\n<\/ul>\n\ud83e\uddea How it works:\n<ul data-spread=\"false\">\n \t<li>Takes text as input (or speech transcription).<\/li>\n \t<li>Outputs: whether a phrase is \"check-worthy\" (needs verification) or not.<\/li>\n<\/ul>\n\ud83d\udcce Used in: FactStream by Duke University.\n\ud83d\udcda <strong>TrustworthyQA<\/strong> Essence: a dataset and model developed to evaluate the reliability of statements made in LLM responses.\n\ud83d\udccc Where it's useful:\n<ul data-spread=\"false\">\n \t<li>as an additional layer in the text generation pipeline;<\/li>\n \t<li>for training models on \"suspicious\" queries (e.g., \"Does Bill Gates control the weather?\").<\/li>\n<\/ul>\n\ud83e\udde0 What makes it interesting:\n<ul data-spread=\"false\">\n \t<li>It doesn't just check facts, but evaluates the reliability of AI responses to potentially dubious questions.<\/li>\n \t<li>The model learns to say \"I don't know\" or indicate that information is controversial.<\/li>\n<\/ul>\n<div><\/div>\n<strong>Recommendations for different target groups<\/strong>\n<strong>Those studying information operations<\/strong>\n<ul data-spread=\"false\">\n \t<li>Attention to sources created not for humans, but for machines.<\/li>\n \t<li>Monitoring artificial networks with suspiciously uniform content.<\/li>\n<\/ul>\n<strong>LLM developers<\/strong>\n<ul data-spread=\"false\">\n \t<li>Pay attention to the origin of training data.<\/li>\n \t<li>Embed fact-checking modules and reliability assessment systems (e.g., TrustworthyQA).<\/li>\n \t<li>Use filters like ClaimBuster (or their equivalents for Cyrillic languages) at the data preprocessing stage.<\/li>\n<\/ul>\n<strong>Fact-checkers and journalists<\/strong>\n<ul data-spread=\"false\">\n \t<li>Identify multiple publications of the same texts under different domains.<\/li>\n \t<li>Compare where LLMs draw examples and quotes from.<\/li>\n \t<li>Apply parsers and tools for analyzing network structures (e.g., trafilatura, Graphistry).<\/li>\n<\/ul>\n<div><\/div>\nThe phenomenon of LLM grooming is not only a new front in disinformation but also a challenge for developers and regulators. Content farms are being created that impact not the audience, but machines. Combating these processes requires new approaches to data auditing, indexing, and LLM training trends.\n<em>The sooner we learn to recognize LLM Grooming, the better we can protect the information environment of the future.<\/em>","_eng_post_name":"","_eng_post_excerpt":"","_eng_post_title":"LLM Grooming as a New Threat: How Pro-Kremlin Networks Prepare Information for AI","_bel_post_content":"\u0428\u0442\u043e \u0442\u0430\u043a\u043e\u0435 LLM Grooming - \u043c\u0430\u043d\u0456\u043f\u0443\u043b\u044f\u0446\u044b\u044f \u043d\u0430\u0432\u0443\u0447\u0430\u043b\u044c\u043d\u044b\u043c \u0430\u0441\u044f\u0440\u043e\u0434\u0434\u0437\u0435\u043c \u0428\u0406 \u0430\u0431\u043e \u043f\u0430\u0434\u043c\u0435\u043d\u0430 \u043d\u0430\u0432\u0443\u0447\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445 \u0434\u043b\u044f \u0428\u0406.\n\u0423\u044f\u0432\u0456\u0446\u0435, \u0448\u0442\u043e \u043d\u0435\u0445\u0442\u0430 \u043c\u0430\u0441\u0430\u0432\u0430 \u0437\u0430\u0441\u0435\u0439\u0432\u0430\u0435 \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0442 \u0430\u0440\u0442\u044b\u043a\u0443\u043b\u0430\u043c\u0456, \u0431\u043b\u043e\u0433\u0430\u043c\u0456, \u0444\u044d\u0439\u043a\u0430\u0432\u044b\u043c\u0456 \u043d\u0430\u0432\u0456\u043d\u0430\u043c\u0456, \u0441\u043f\u0435\u0446\u044b\u044f\u043b\u044c\u043d\u0430 \u043d\u0430\u043f\u0456\u0441\u0430\u043d\u044b\u043c\u0456 \u0434\u043b\u044f \u0442\u0430\u0433\u043e, \u043a\u0430\u0431 \u043d\u0435 \u0432\u044b \u0456\u0445 \u0447\u044b\u0442\u0430\u043b\u0456, \u0430 \u0428\u0406.\n\u0413\u044d\u0442\u0430 \u043d\u0435 \u0434\u043b\u044f \u043a\u043b\u0456\u043a\u0430\u045e \u0456 \u043b\u0430\u0439\u043a\u0430\u045e, \u0430 \u043a\u0430\u0431 \u043f\u0430\u045e\u043f\u043b\u044b\u0432\u0430\u0446\u044c \u043d\u0430 \u0442\u043e\u0435, \u044f\u043a \u0434\u0443\u043c\u0430\u044e\u0446\u044c \u0431\u0443\u0434\u0443\u0447\u044b\u044f \u043f\u0430\u043a\u0430\u043b\u0435\u043d\u043d\u0456 ChatGPT, Claude, Gemini \u0456 \u0456\u043d\u0448\u044b\u0445 \u043c\u0430\u0434\u044d\u043b\u044f\u045e.\n\u0413\u044d\u0442\u0430 \u0456 \u0451\u0441\u0446\u044c LLM grooming \u2014 \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0439\u043d\u0430\u0435 \u043f\u0440\u0430\u0433\u0440\u0430\u043c\u0430\u0432\u0430\u043d\u043d\u0435 \u0448\u0442\u0443\u0447\u043d\u0430\u0433\u0430 \u0456\u043d\u0442\u044d\u043b\u0435\u043a\u0442\u0443 \u043f\u0440\u0430\u0437 \"\u043f\u0430\u0434\u0440\u044b\u0445\u0442\u0430\u0432\u0430\u043d\u044b\u044f\" \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f.\nLLM grooming \u2014 \u0433\u044d\u0442\u0430 \u0441\u0442\u0440\u0430\u0442\u044d\u0433\u0456\u044f, \u043f\u0440\u044b \u044f\u043a\u043e\u0439 \u0437\u043b\u0430\u043c\u044b\u0441\u043d\u0456\u043a\u0456 \u043c\u0430\u0441\u0430\u0432\u0430 \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u044e\u0446\u044c \u0443 \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0446\u0435 \u0456\u043b\u0436\u044b\u0432\u0443\u044e \u0430\u0431\u043e \u043c\u0430\u043d\u0456\u043f\u0443\u043b\u044f\u0442\u044b\u045e\u043d\u0443\u044e \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044e, \u043f\u0440\u044b\u0437\u043d\u0430\u0447\u0430\u043d\u0443\u044e \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0437\u0435\u0439, \u0430 \u0434\u043b\u044f \u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0447\u043d\u044b\u0445 \u0441\u0456\u0441\u0442\u044d\u043c \u0437\u0431\u043e\u0440\u0443 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445, \u0432\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0435\u043c\u044b\u0445 \u043f\u0440\u044b \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0456 \u0432\u044f\u043b\u0456\u043a\u0456\u0445 \u043c\u043e\u045e\u043d\u044b\u0445 \u043c\u0430\u0434\u044d\u043b\u044f\u045e (LLM).\n\u0413\u044d\u0442\u0430 \u043d\u0435 \u0434\u043b\u044f \u043a\u043b\u0456\u043a\u0430\u045e \u0456 \u043b\u0430\u0439\u043a\u0430\u045e, \u0430 \u043a\u0430\u0431 \u043f\u0430\u045e\u043f\u043b\u044b\u0432\u0430\u0446\u044c \u043d\u0430 \u0442\u043e\u0435, \u044f\u043a \"\u0434\u0443\u043c\u0430\u044e\u0446\u044c\" \u0431\u0443\u0434\u0443\u0447\u044b\u044f \u043f\u0430\u043a\u0430\u043b\u0435\u043d\u043d\u0456 ChatGPT, Claude, Gemini \u0456 \u0456\u043d\u0448\u044b\u0445 \u043c\u0430\u0434\u044d\u043b\u044f\u045e.\n\u041c\u044d\u0442\u0430 \u2014 \u0443\u043a\u0430\u0440\u0430\u043d\u0456\u0446\u044c \u043f\u044d\u045e\u043d\u044b\u044f \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u044b \u045e \u043c\u0430\u0434\u044d\u043b\u0456, \u043a\u0430\u0431 \u044f\u043d\u044b \u045e\u0437\u043d\u0430\u045e\u043b\u044f\u043b\u0456 \u0456\u0445 \u0443 \u0441\u0432\u0430\u0456\u0445 \u0430\u0434\u043a\u0430\u0437\u0430\u0445. \u0413\u044d\u0442\u0430 \u043c\u043e\u0436\u0430 \u043f\u0440\u044b\u0432\u0435\u0441\u0446\u0456 \u0434\u0430 \u0441\u043a\u0430\u0436\u044d\u043d\u043d\u044f \u045e\u044f\u045e\u043b\u0435\u043d\u043d\u044f\u045e \u0430\u0431 \u0433\u0456\u0441\u0442\u0430\u0440\u044b\u0447\u043d\u044b\u0445 \u043f\u0430\u0434\u0437\u0435\u044f\u0445, \u043f\u0430\u043b\u0456\u0442\u044b\u0446\u044b, \u0433\u0435\u0430\u0433\u0440\u0430\u0444\u0456\u0456, \u0430\u0445\u043e\u0432\u0435 \u0437\u0434\u0430\u0440\u043e\u045e\u044f \u0456 \u0456\u043d\u0448.\nLLM Grooming \u2014 \u0433\u044d\u0442\u0430 \u0430\u0442\u0430\u043a\u0430 \u043d\u0435 \u043d\u0430 \u0441\u0432\u044f\u0434\u043e\u043c\u0430\u0441\u0446\u044c \u0447\u0430\u043b\u0430\u0432\u0435\u043a\u0430, \u0430 \u043d\u0430 \u0430\u0440\u0445\u0456\u0442\u044d\u043a\u0442\u0443\u0440\u0443 \u0432\u0435\u0434\u0430\u045e, \u044f\u043a\u0443\u044e \u0431\u0443\u0434\u0443\u044e\u0446\u044c \u043c\u043e\u045e\u043d\u044b\u044f \u043c\u0430\u0434\u044d\u043b\u0456.\n<strong>\u042f\u043a \u0433\u044d\u0442\u0430 \u0432\u044b\u0433\u043b\u044f\u0434\u0430\u0435 \u043d\u0430 \u043f\u0440\u0430\u043a\u0442\u044b\u0446\u044b<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0423 \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0446\u0435 \u043c\u0430\u0441\u0430\u0432\u0430 \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u044e\u0446\u0446\u0430 \u0430\u0440\u0442\u044b\u043a\u0443\u043b\u044b, \u0431\u043b\u043e\u0433\u0456, \u0444\u044d\u0439\u043a\u0430\u0432\u044b\u044f \u043d\u0430\u0432\u0456\u043d\u044b, \u043d\u0430\u043f\u0456\u0441\u0430\u043d\u044b\u044f \u0442\u0430\u043a, \u043a\u0430\u0431 \u043d\u0435 \u0431\u044b\u0446\u044c \u0446\u0456\u043a\u0430\u0432\u044b\u043c\u0456 \u043a\u0430\u0440\u044b\u0441\u0442\u0430\u043b\u044c\u043d\u0456\u043a\u0443, \u0430\u043b\u0435 \u043f\u0430\u0434\u044b\u0445\u043e\u0434\u0437\u0456\u0446\u044c \u043f\u0430\u0434 \u0430\u043b\u0433\u0430\u0440\u044b\u0442\u043c\u044b \u0437\u0431\u043e\u0440\u0443 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445.<\/li>\n \t<li>\u0410\u0434\u0437\u0456\u043d \u0456 \u0442\u043e\u0439 \u0436\u0430 \u0442\u044d\u043a\u0441\u0442 \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u0435\u0446\u0446\u0430 \u043f\u0430\u0434 \u0432\u044b\u0433\u043b\u044f\u0434\u0430\u043c \u043d\u0435\u0437\u0430\u043b\u0435\u0436\u043d\u044b\u0445 \u043a\u0440\u044b\u043d\u0456\u0446, \u0441\u0442\u0432\u0430\u0440\u0430\u044e\u0447\u044b \u0456\u043b\u044e\u0437\u0456\u044e \u045e\u0437\u0433\u043e\u0434\u043d\u0435\u043d\u0430\u0441\u0446\u0456.<\/li>\n \t<li>\u0410\u0441\u043d\u043e\u045e\u043d\u0430\u044f \u043f\u0440\u044b\u043a\u043c\u0435\u0442\u0430: \u0434\u0440\u044d\u043d\u043d\u044b UX, \u0430\u0434\u0441\u0443\u0442\u043d\u0430\u0441\u0446\u044c \u043a\u0430\u043c\u0435\u043d\u0442\u0430\u0440\u043e\u045e, \u043f\u0440\u0430\u0437\u043c\u0435\u0440\u043d\u0430\u044f \u043f\u0430\u045e\u0442\u0430\u0440\u0430\u043b\u044c\u043d\u0430\u0441\u0446\u044c, \u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0447\u043d\u044b\u044f \u043f\u0435\u0440\u0430\u043a\u043b\u0430\u0434\u044b, SEO-\u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u2014 \u0443\u0441\u0451, \u043a\u0430\u0431 \"\u0441\u043f\u0430\u0434\u0430\u0431\u0430\u0446\u0446\u0430\" \u043f\u0430\u0440\u0441\u0435\u0440\u0430\u043c \u0456 \u0431\u043e\u0442\u0430\u043c.<\/li>\n<\/ul>\n\ud83e\udde8 \u042f\u043a \u0433\u044d\u0442\u0430 \u043c\u043e\u0436\u0430 \u0432\u044b\u0433\u043b\u044f\u0434\u0430\u0446\u044c?\n<ul data-spread=\"false\">\n \t<li>\u041d\u0430 \u0432\u044b\u0433\u043b\u044f\u0434 \u2014 \u0437\u0432\u044b\u0447\u0430\u0439\u043d\u044b \u0431\u043b\u043e\u0433 \u0430\u0431\u043e \u0444\u043e\u0440\u0443\u043c, \u0430\u043b\u0435 \u045e\u0441\u0435 \u043f\u0430\u0441\u0442\u044b \u0432\u044b\u0433\u043b\u044f\u0434\u0430\u044e\u0446\u044c \u0437\u0430\u043d\u0430\u0434\u0442\u0430 \u0430\u0434\u043d\u0430\u0442\u044b\u043f\u043d\u0430, \u043f\u0430\u045e\u0442\u0430\u0440\u0430\u044e\u0446\u044c \u0430\u0434\u043d\u0443 \u0456 \u0442\u0443\u044e \u0436 \u0432\u0435\u0440\u0441\u0456\u044e \u043f\u0430\u0434\u0437\u0435\u0439.<\/li>\n \t<li>\u041d\u0435\u0445\u0442\u0430 \u0441\u0442\u0432\u0430\u0440\u0430\u0435 \u0441\u043e\u0442\u043d\u0456 \u0430\u0440\u0442\u044b\u043a\u0443\u043b\u0430\u045e \u043f\u0440\u0430 \u00ab\u0432\u0443\u0447\u043e\u043d\u044b\u0445, \u044f\u043a\u0456\u044f \u0434\u0430\u043a\u0430\u0437\u0430\u043b\u0456 \u0448\u043a\u043e\u0434\u0443 \u044d\u043b\u0435\u043a\u0442\u0440\u044b\u0447\u043d\u0430\u0441\u0446\u0456\u00bb \u2014 \u043d\u0435 \u0434\u043b\u044f \u0444\u044d\u0439\u043a\u0430\u0432\u0430\u0433\u0430 \u0441\u0440\u0430\u043a\u0443 \u045e \u043a\u0430\u043c\u0435\u043d\u0442\u0430\u0445, \u0430 \u043a\u0430\u0431 \u044f\u043d\u044b \u0442\u0440\u0430\u043f\u0456\u043b\u0456 \u045e \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u044b \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u044f LLM.<\/li>\n \t<li>\u0423 TikTok \u0437'\u044f\u045e\u043b\u044f\u044e\u0446\u0446\u0430 \"\u0434\u043e\u043a\u0430\u0437\u044b\", \u0448\u0442\u043e \u00ab\u041f\u043e\u043b\u044c\u0448\u0447\u0430 \u0431\u044b\u043b\u0430 \u0447\u0430\u0441\u0442\u043a\u0430\u0439 \u0420\u0430\u0441\u0456\u0456\u00bb \u2014 \u0437\u043d\u043e\u045e \u0436\u0430, \u043d\u0435 \u0434\u0437\u0435\u043b\u044f \u043f\u0440\u0430\u0433\u043b\u044f\u0434\u0430\u045e, \u0430 \u0434\u0437\u0435\u043b\u044f \u0456\u043d\u0434\u044d\u043a\u0441\u0430\u0446\u044b\u0456.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f \u042f\u043a \u0432\u044b\u044f\u0432\u0456\u0446\u044c LLM Grooming? (\u043c\u0435\u0442\u0430\u0434\u044b \u0441\u0432\u044f\u0434\u043e\u043c\u0430\u0433\u0430 \u0430\u045e\u0434\u044b\u0442\u0443)\n<ol start=\"1\" data-spread=\"true\">\n \t<li>\u0421\u043b\u0443\u0445\u0430\u0439, \u0448\u0442\u043e \u043a\u0430\u0436\u0443\u0446\u044c \u043c\u0430\u0434\u044d\u043b\u0456\n<ul data-spread=\"false\">\n \t<li>\u0417\u0430\u0434\u0430\u0432\u0430\u0439 \u0430\u0434\u0447\u0443\u0432\u0430\u043b\u044c\u043d\u044b\u044f \u043f\u044b\u0442\u0430\u043d\u043d\u0456 \u0456 \u0433\u043b\u044f\u0434\u0437\u0456, \u0446\u0456 \u043d\u0435 \u0445\u0456\u043b\u0456\u0446\u044c \u0430\u0434\u043a\u0430\u0437 \u0443 \u0430\u0434\u0437\u0456\u043d \u0431\u043e\u043a, \u0430\u0441\u0430\u0431\u043b\u0456\u0432\u0430 \u043a\u0430\u043b\u0456 \u0433\u044d\u0442\u0430 \u043d\u0435 \u0430\u0434\u043f\u0430\u0432\u044f\u0434\u0430\u0435 \u0431\u0430\u043b\u0430\u043d\u0441\u0443 \u045e \u0440\u044d\u0430\u043b\u044c\u043d\u0430\u0439 \u044d\u043a\u0441\u043f\u0435\u0440\u0442\u043d\u0430\u0439 \u0441\u0443\u043f\u043e\u043b\u044c\u043d\u0430\u0441\u0446\u0456.<\/li>\n \t<li>\u041f\u0430\u0440\u0430\u045e\u043d\u043e\u045e\u0432\u0430\u0439 \u043f\u0430\u0432\u043e\u0434\u0437\u0456\u043d\u044b \u043c\u0430\u0434\u044d\u043b\u044f\u045e \u0430\u0434 \u0440\u043e\u0437\u043d\u044b\u0445 \u0440\u0430\u0441\u043f\u0440\u0430\u0446\u043e\u045e\u0448\u0447\u044b\u043a\u0430\u045e: GPT, Claude, Gemini. \u041a\u0430\u043b\u0456 \u045e\u0441\u0435 \u00ab\u0432\u044f\u0434\u0443\u0446\u0446\u0430\u00bb \u043d\u0430 \u0430\u0434\u0437\u0456\u043d \u043d\u0430\u0440\u0430\u0442\u044b\u045e \u2014 \u043c\u0430\u0433\u0447\u044b\u043c\u0430, \u0451\u043d \u043f\u0440\u0430\u0439\u0448\u043e\u045e \u043f\u0440\u0430\u0437 \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u044b.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u0421\u0430\u0447\u044b \u0437\u0430 \u043a\u0440\u044b\u043d\u0456\u0446\u0430\u043c\u0456\n<ul data-spread=\"false\">\n \t<li>\u041a\u0430\u043b\u0456 \u043c\u0430\u0434\u044d\u043b\u044c \u0441\u043f\u0430\u0441\u044b\u043b\u0430\u0435\u0446\u0446\u0430 \u043d\u0430 \u043c\u0430\u0440\u0433\u0456\u043d\u0430\u043b\u044c\u043d\u044b\u044f \u0430\u0431\u043e \u0430\u0434\u043d\u0430\u0442\u044b\u043f\u043d\u044b\u044f \u0441\u0430\u0439\u0442\u044b \u2014 \u0442\u0440\u044b\u0432\u043e\u0436\u043d\u044b \u0437\u0432\u0430\u043d\u043e\u0447\u0430\u043a.<\/li>\n \t<li>\u041c\u043e\u0436\u043d\u0430 \u0432\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0446\u044c \u043f\u0440\u0430\u0441\u043b\u043e\u0439\u043a\u0443 \u0430\u043d\u0430\u043b\u0456\u0437\u0443 (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043f\u0430\u0434\u043a\u043b\u044e\u0447\u044b\u0446\u044c \u0431\u0456\u0431\u043b\u0456\u044f\u0442\u044d\u043a\u0456 trafilatura, newspaper3k) \u0456 \u043f\u0430\u0433\u043b\u044f\u0434\u0437\u0435\u0446\u044c, \u044f\u043a\u0456\u044f \u0434\u0430\u043c\u0435\u043d\u044b \u045e\u0441\u043f\u043b\u044b\u0432\u0430\u044e\u0446\u044c \u0447\u0430\u0441\u0446\u0435\u0439 \u0437\u0430 \u045e\u0441\u0451.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u0413\u043b\u044f\u0434\u0437\u0456 \u043d\u0430 \u0448\u0443\u043c \u0443 \u0456\u043d\u0444\u0430\u043f\u0440\u0430\u0441\u0442\u043e\u0440\u044b\n<ul data-spread=\"false\">\n \t<li>\u041a\u0430\u043b\u0456 \u0440\u0430\u043f\u0442\u0430\u043c \u0437'\u044f\u045e\u043b\u044f\u0435\u0446\u0446\u0430 \u045e\u0441\u043f\u043b\u0435\u0441\u043a \u043f\u0430\u0434\u043e\u0431\u043d\u044b\u0445 \u0442\u044d\u043a\u0441\u0442\u0430\u045e, \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 \u0430\u0431\u043e \u0442\u044d\u0440\u043c\u0456\u043d\u0430\u045e \u2014 \u0433\u044d\u0442\u0430 \u043c\u043e\u0436\u0430 \u0431\u044b\u0446\u044c \u0441\u043f\u0440\u043e\u0431\u0430 \u043c\u0430\u0441\u0456\u0440\u0430\u0432\u0430\u043d\u0430\u0433\u0430 \u045e\u043a\u0456\u0434\u0443 \u045e \u043f\u043e\u0448\u0443\u043a\u0430\u0432\u0443\u044e \u0441\u0456\u0441\u0442\u044d\u043c\u0443 \u0456, \u043f\u0440\u0430\u0437 \u044f\u0435, \u0443 \u0431\u0443\u0434\u0443\u0447\u0430\u0435 \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0435 LLM.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\ud83e\uddf7 \u042f\u043a \u0430\u0431\u0430\u0440\u0430\u043d\u044f\u0446\u0446\u0430? (\u043a\u0430\u043b\u0456 \u0442\u044b \u0434\u0430\u0441\u043b\u0435\u0434\u0447\u044b\u043a, \u0440\u0430\u0441\u043f\u0440\u0430\u0446\u043e\u045e\u0448\u0447\u044b\u043a, \u0430\u0431\u043e \u043f\u0440\u043e\u0441\u0442\u0430 \u043d\u0435\u0430\u0431\u044b\u044f\u043a\u0430\u0432\u044b) \ud83d\udd12 \u041a\u0430\u043b\u0456 \u0442\u044b \u043f\u0440\u0430\u0446\u0443\u0435\u0448 \u0437 \u0428\u0406:\n<ul data-spread=\"false\">\n \t<li>\u0421\u0430\u0447\u044b \u0437\u0430 \u043c\u0435\u0442\u0430-\u043a\u0430\u043d\u0442\u044d\u043d\u0442\u0430\u043c: \u044f\u043a\u0456\u044f \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f \u0442\u0440\u0430\u043f\u043b\u044f\u044e\u0446\u044c \u0443 \u0442\u0432\u0430\u0435 \u043c\u0430\u0434\u044d\u043b\u0456? \u0412\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0439 \u0444\u0456\u043b\u044c\u0442\u0440\u044b, \u043f\u0440\u0430\u0432\u044f\u0440\u0430\u0439 \u0434\u0430\u043c\u0435\u043d\u044b, \u0432\u0430\u043b\u0456\u0434\u0443\u0439 \u043a\u0440\u044b\u043d\u0456\u0446\u044b.<\/li>\n \t<li>\u0414\u0430\u0434\u0430\u0432\u0430\u0439 \u0441\u043b\u0430\u0456 \u0444\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433\u0443: \u0444\u0456\u043b\u044c\u0442\u0440\u044b \u0442\u044b\u043f\u0443 ClaimBuster, \u043c\u0430\u0434\u044d\u043b\u0456 \u0442\u044b\u043f\u0443 TrustworthyQA.<\/li>\n<\/ul>\n\ud83d\udd75\ufe0f \u041a\u0430\u043b\u0456 \u0442\u044b \u0436\u0443\u0440\u043d\u0430\u043b\u0456\u0441\u0442, \u0430\u043a\u0442\u044b\u0432\u0456\u0441\u0442 \u0430\u0431\u043e \u0430\u043d\u0430\u043b\u0456\u0442\u044b\u043a:\n<ul data-spread=\"false\">\n \t<li>\u0423\u043a\u0430\u0440\u0430\u043d\u0456 \u043c\u0430\u043d\u0456\u0442\u043e\u0440\u044b\u043d\u0433 \u043a\u043b\u044e\u0447\u0430\u0432\u044b\u0445 \u0442\u044d\u043c \u0443 Google, TikTok, Telegram, YouTube.<\/li>\n \t<li>\u0413\u043b\u044f\u0434\u0437\u0456, \u0446\u0456 \u043d\u0435 \u043f\u0430\u045e\u0442\u0430\u0440\u0430\u044e\u0446\u0446\u0430 \u043a\u043b\u044e\u0447\u0430\u0432\u044b\u044f \u0444\u0440\u0430\u0437\u044b, \u043f\u0430\u0442\u044d\u0440\u043d\u044b \u0444\u0430\u0440\u043c\u0443\u043b\u0451\u0432\u0430\u043a \u2014 \u0433\u044d\u0442\u0430 \u043c\u043e\u0436\u0430 \u0431\u044b\u0446\u044c \u0441\u0456\u0433\u043d\u0430\u043b\u0430\u043c \"\u0456\u043d\u0444\u0430\u0437\u0430\u0441\u0435\u0432\u0443\".<\/li>\n<\/ul>\n\ud83d\udcac \u041a\u0430\u043b\u0456 \u0442\u044b \u043f\u0440\u043e\u0441\u0442\u0430 \u043a\u0430\u0440\u044b\u0441\u0442\u0430\u043b\u044c\u043d\u0456\u043a:\n<ul data-spread=\"false\">\n \t<li>\u0411\u0443\u0434\u0437\u044c \u0441\u043a\u0435\u043f\u0442\u044b\u0447\u043d\u044b\u043c. \u041d\u0430\u0432\u0430\u0442 \u043a\u0430\u043b\u0456 \u0428\u0406 \u0448\u0442\u043e\u0441\u044c\u0446\u0456 \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u0430\u0435 \u2014 \u043f\u0440\u0430\u0432\u0435\u0440\u044b.<\/li>\n \t<li>\u041f\u0430\u043c\u044f\u0442\u0430\u0439: \u0428\u0406 \u043c\u043e\u0436\u0430 \u00ab\u043f\u0430\u045e\u0442\u0430\u0440\u0430\u0446\u044c\u00bb \u0437\u0430 \u0442\u044b\u043c\u0456, \u0445\u0442\u043e \u0433\u0440\u0430\u043c\u0447\u044d\u0439, \u0430 \u043d\u0435 \u0437\u0430 \u0442\u044b\u043c\u0456, \u0445\u0442\u043e \u043c\u0430\u0435 \u0440\u0430\u0446\u044b\u044e.<\/li>\n<\/ul>\n\ud83d\udccc \u0428\u0442\u043e \u0434\u0430\u043b\u0435\u0439?\n<ul data-spread=\"false\">\n \t<li>LLM grooming \u2014 \u0433\u044d\u0442\u0430 \u043d\u043e\u0432\u044b \u0432\u0435\u043a\u0442\u0430\u0440 FIMI (foreign information manipulation and interference). \u0401\u043d \u043d\u0435\u0437\u0430\u045e\u0432\u0430\u0436\u043d\u044b, \u0446\u0456\u0445\u0456, \u043f\u0440\u0430\u0446\u0443\u0435 \u043d\u0430 \u0434\u043e\u045e\u0433\u0443\u044e \u0433\u0443\u043b\u044c\u043d\u044e.<\/li>\n \t<li>\u041c\u0435\u043d\u0430\u0432\u0456\u0442\u0430 \u0442\u0430\u043c\u0443 \u0432\u0430\u0436\u043d\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u0456 \u0430\u0431\u0430\u0440\u0430\u043d\u044f\u0446\u044c \u0428\u0406 \u0430\u0434 \u0444\u044d\u0439\u043a\u0430\u045e, \u0430\u043b\u0435 \u0456 \u0432\u0443\u0447\u044b\u0446\u044c \u044f\u0433\u043e \u043a\u0440\u044b\u0442\u044b\u0447\u043d\u0430 \u043c\u044b\u0441\u043b\u0456\u0446\u044c \u2014 \u0442\u0430\u043a \u0436\u0430, \u044f\u043a \u043b\u044e\u0434\u0437\u0435\u0439.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u041a\u0435\u0439\u0441 Pravda Network: \u0441\u0435\u0442\u043a\u0456 \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0437\u0435\u0439<\/strong>\n\u0423 <a href=\"https:\/\/static1.squarespace.com\/static\/6612cbdfd9a9ce56ef931004\/t\/67fd396818196f3d1666bc23\/1744648558879\/PK+Report.pdf\">\u0441\u043f\u0440\u0430\u0432\u0430\u0437\u0434\u0430\u0447\u044b<\/a> American Sunlight Project (\u043b\u044e\u0442\u044b 2025) \u0430\u043f\u0456\u0441\u0430\u043d\u0430 \u0434\u0437\u0435\u0439\u043d\u0430\u0441\u0446\u044c \u0441\u0435\u0442\u043a\u0456 Pravda Network \u2014 \u0447\u0430\u0441\u0442\u043a\u0456 \u0431\u043e\u043b\u044c\u0448 \u0448\u044b\u0440\u043e\u043a\u0430\u0439 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u044b \u043f\u0430\u0434 \u043d\u0430\u0437\u0432\u0430\u0439 \"Portal Kombat\". \u0413\u044d\u0442\u0430 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0430 \u045e\u043a\u043b\u044e\u0447\u0430\u0435 \u0434\u0430\u043c\u0435\u043d\u044b \u0456 \u043f\u0430\u0434\u0434\u0430\u043c\u0435\u043d\u044b, \u044f\u043a\u0456\u044f \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u044e\u0446\u044c \u0456\u0434\u044d\u043d\u0442\u044b\u0447\u043d\u044b\u044f \u0442\u044d\u043a\u0441\u0442\u044b \u0437 \u043f\u0440\u0430\u0440\u0430\u0441\u0456\u0439\u0441\u043a\u0456\u043c\u0456 \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u0430\u043c\u0456, \u043d\u0430 \u0440\u043e\u0437\u043d\u044b\u0445 \u043c\u043e\u0432\u0430\u0445 \u0456 \u043f\u0430\u0434 \u0440\u043e\u0437\u043d\u044b\u043c\u0456 \u0431\u0440\u044d\u043d\u0434\u0430\u043c\u0456.\n\u0413\u044d\u0442\u044b\u044f \u0441\u0430\u0439\u0442\u044b:\n<ul data-spread=\"false\">\n \t<li>\u0430\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u044b \u044f\u043a \u043d\u0430\u0432\u0456\u043d\u0430\u0432\u044b\u044f \u043a\u0440\u044b\u043d\u0456\u0446\u044b,<\/li>\n \t<li>\u043c\u0430\u044e\u0446\u044c \u0430\u0431\u043c\u0435\u0436\u0430\u0432\u0430\u043d\u0443\u044e \u0444\u0443\u043d\u043a\u0446\u044b\u044f\u043d\u0430\u043b\u044c\u043d\u0430\u0441\u0446\u044c \u0434\u043b\u044f \u0447\u0430\u043b\u0430\u0432\u0435\u043a\u0430 (\u0434\u0440\u044d\u043d\u043d\u0430\u044f \u043d\u0430\u0432\u0456\u0433\u0430\u0446\u044b\u044f, \u043d\u044f\u0437\u0440\u0443\u0447\u043d\u0430\u0435 \u0430\u0444\u0430\u0440\u043c\u043b\u0435\u043d\u043d\u0435),<\/li>\n \t<li>\u0445\u0443\u0442\u0447\u044d\u0439 \u0437\u0430 \u045e\u0441\u0451, \u043f\u0440\u044b\u0437\u043d\u0430\u0447\u0430\u043d\u044b \u0434\u043b\u044f \u0456\u043d\u0434\u044d\u043a\u0441\u0430\u0446\u044b\u0456 \u0448\u0442\u0443\u0447\u043d\u044b\u043c\u0456 \u043c\u0430\u0434\u044d\u043b\u044f\u043c\u0456.<\/li>\n<\/ul>\n\u0417\u0433\u043e\u0434\u043d\u0430 \u0437 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u043c\u0456 \u0434\u0430\u0448\u0431\u043e\u0440\u0434\u0430 <a href=\"https:\/\/portal-kombat.com\/\">portal-kombat.com<\/a>, \u0441\u0435\u0442\u043a\u0430 \u045e\u043a\u043b\u044e\u0447\u0430\u0435 182 \u0441\u0430\u0439\u0442\u0430, \u044f\u043a\u0456\u044f \u0441\u043f\u0430\u043b\u0443\u0447\u0430\u044e\u0446\u044c \u0430\u0434\u043d\u044b \u0456 \u0442\u044b\u044f \u0436 \u0442\u044d\u043a\u0441\u0442\u044b \u0437 \u0440\u043e\u0437\u043d\u044b\u043c\u0456 \u043c\u043e\u045e\u043d\u044b\u043c\u0456 \u043c\u0435\u0442\u0430\u0434\u0430\u0434\u0437\u0435\u043d\u044b\u043c\u0456.\n<img class=\"alignnone size-full wp-image-3344\" src=\"https:\/\/factcheck.lt\/wp-content\/uploads\/2025\/04\/pravda-sakartvelo-scaled.png\" alt=\"\" width=\"2560\" height=\"1311\" \/>\n\u0414\u0430\u0448\u0431\u043e\u0440\u0434 \u0430\u0434\u043b\u044e\u0441\u0442\u0440\u043e\u045e\u0432\u0430\u0435 \u0441\u043f\u0456\u0441 \u0434\u0430\u043c\u0435\u043d\u0430\u045e, \u0456\u0445 \u0434\u0430\u0442\u0443 \u0440\u044d\u0433\u0456\u0441\u0442\u0440\u0430\u0446\u044b\u0456, \u0437\u0430\u0440\u044d\u0433\u0456\u0441\u0442\u0440\u0430\u0432\u0430\u045e\u0448\u0443\u044e \u043a\u0440\u0430\u0456\u043d\u0443 \u0456 \u043a\u0430\u043c\u0443\u043d\u0456\u043a\u0430\u0446\u044b\u0439\u043d\u0443\u044e \"\u0441\u0444\u0435\u0440\u0443\" (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043d\u0430\u0446\u044b\u044f\u043d\u0430\u043b\u044c\u043d\u0443\u044e \u043f\u0440\u044b\u043d\u0430\u043b\u0435\u0436\u043d\u0430\u0441\u0446\u044c \u0441\u0430\u0439\u0442\u0430). \u0413\u044d\u0442\u0430 \u0456\u043d\u0442\u044d\u0440\u0430\u043a\u0442\u044b\u045e\u043d\u044b \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442, \u044f\u043a\u0456 \u0434\u0430\u0437\u0432\u0430\u043b\u044f\u0435 \u0434\u0430\u0441\u043b\u0435\u0434\u0447\u044b\u043a\u0430\u043c \u0430\u0434\u0441\u043e\u0447\u0432\u0430\u0446\u044c \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440\u0443 \u0441\u0435\u0442\u043a\u0456, \u043c\u0430\u0448\u0442\u0430\u0431\u044b \u0430\u0445\u043e\u043f\u0443 \u0456 \u0440\u0430\u0441\u043f\u0430\u045e\u0441\u044e\u0434\u0436\u0432\u0430\u043d\u043d\u0435 \u043a\u043b\u044e\u0447\u0430\u0432\u044b\u0445 \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u0430\u045e.\n<div><\/div>\n<strong>\u041c\u0430\u0442\u044b\u0432\u044b \u0441\u0435\u0442\u043a\u0456<\/strong>\n\u0423 \u043c\u0456\u043d\u0443\u043b\u044b\u0445 \u043f\u0443\u0431\u043b\u0456\u043a\u0430\u0446\u044b\u044f\u0445 \u0430\u0431 \u043f\u0430\u0442\u044d\u043d\u0446\u044b\u0439\u043d\u044b\u0445 \u043c\u0430\u0442\u044b\u0432\u0430\u0445 \u0441\u0435\u0442\u043a\u0456 \u00ab\u041f\u0440\u0430\u045e\u0434\u0430\u00bb \u0430\u0441\u043d\u043e\u045e\u043d\u0430\u044f \u045e\u0432\u0430\u0433\u0430 \u043d\u0430\u0434\u0430\u0432\u0430\u043b\u0430\u0441\u044f \u044f\u0435 \u0430\u043d\u0442\u044b\u045e\u043a\u0440\u0430\u0456\u043d\u0441\u043a\u0430\u043c\u0443 \u0456 \u043f\u0440\u0430\u0432\u0430\u0435\u043d\u043d\u0430\u043c\u0443 \u0445\u0430\u0440\u0430\u043a\u0442\u0430\u0440\u0443, \u0430 \u0442\u0430\u043a\u0441\u0430\u043c\u0430 \u043c\u0430\u0433\u0447\u044b\u043c\u044b\u043c \u043d\u0430\u0441\u0442\u0443\u043f\u0441\u0442\u0432\u0430\u043c \u0434\u043b\u044f \u0435\u045e\u0440\u0430\u043f\u0435\u0439\u0441\u043a\u0456\u0445 \u0432\u044b\u0431\u0430\u0440\u0430\u045e 2024 \u0433\u043e\u0434\u0430. \u0410\u0434\u043d\u0430\u043a, \u043f\u0430\u043a\u043e\u043b\u044c\u043a\u0456 \u0433\u044d\u0442\u0430 \u0441\u0435\u0442\u043a\u0430 \u043f\u0440\u0430\u0446\u044f\u0433\u0432\u0430\u0435 \u0440\u0430\u0441\u0446\u0456 \u0456 \u0437\u043c\u044f\u043d\u044f\u0446\u0446\u0430, \u043d\u0435\u0430\u0431\u0445\u043e\u0434\u043d\u0430 \u0431\u043e\u043b\u044c\u0448 \u0434\u0430\u0441\u043a\u0430\u043d\u0430\u043b\u0430\u0435 \u0432\u044b\u0432\u0443\u0447\u044d\u043d\u043d\u0435, \u043a\u0430\u0431 \u0432\u044b\u0437\u043d\u0430\u0447\u044b\u0446\u044c \u043c\u0430\u0433\u0447\u044b\u043c\u0443\u044e \u0442\u0440\u0430\u0435\u043a\u0442\u043e\u0440\u044b\u044e \u044f\u0435 \u0440\u0430\u0437\u0432\u0456\u0446\u0446\u044f. ASP \u0440\u0430\u0437\u0433\u043b\u044f\u0434\u0430\u0435 \u0442\u0440\u044b \u043c\u0430\u0433\u0447\u044b\u043c\u044b\u044f, \u043d\u0435\u0432\u044b\u043a\u043b\u044e\u0447\u0430\u044e\u0447\u044b\u044f \u0430\u0434\u0437\u0456\u043d \u0430\u0434\u043d\u0430\u0433\u043e \u043c\u0430\u0442\u044b\u0432\u044b \u0441\u0442\u0432\u0430\u0440\u044d\u043d\u043d\u044f \u0441\u0435\u0442\u043a\u0456, \u044f\u043a\u0456\u044f \u0437\u0430\u0441\u044f\u0440\u043e\u0434\u0436\u0430\u043d\u044b \u043d\u0430 \u044f\u0435 \u0442\u044d\u0445\u043d\u0430\u043b\u0430\u0433\u0456\u0447\u043d\u044b\u0445 \u0430\u0441\u0430\u0431\u043b\u0456\u0432\u0430\u0441\u0446\u044f\u0445 \u0456 \u043d\u0435\u0434\u0430\u0445\u043e\u043f\u0430\u0445. \u0413\u044d\u0442\u044b\u044f \u043c\u0430\u0442\u044b\u0432\u044b \u043d\u0435 \u043f\u0440\u044b\u0432\u044f\u0437\u0430\u043d\u044b \u0434\u0430 \u043a\u0430\u043d\u043a\u0440\u044d\u0442\u043d\u044b\u0445 \u043a\u0440\u0430\u0456\u043d, \u0440\u044d\u0433\u0456\u0451\u043d\u0430\u045e \u0430\u0431\u043e \u043f\u0430\u043b\u0456\u0442\u044b\u0447\u043d\u044b\u0445 \u043f\u0430\u0434\u0437\u0435\u0439, \u043f\u0430\u043a\u043e\u043b\u044c\u043a\u0456 \u043c\u044d\u0442\u044b \u043f\u0440\u0430\u0440\u0430\u0441\u0456\u0439\u0441\u043a\u0456\u0445 \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0439\u043d\u044b\u0445 \u0430\u043f\u0435\u0440\u0430\u0446\u044b\u0439 \u043c\u043e\u0433\u0443\u0446\u044c \u0437\u043c\u044f\u043d\u044f\u0446\u0446\u0430.\n<strong>\u0422\u043b\u0443\u043c\u0430\u0447\u044d\u043d\u043d\u0435 A: \u043f\u0430\u0434\u0440\u044b\u0445\u0442\u043e\u045e\u043a\u0430 LLM<\/strong>\n\u041d\u0430\u0439\u0431\u043e\u043b\u044c\u0448 \u0437\u043d\u0430\u0447\u043d\u044b\u043c \u0432\u044b\u043d\u0456\u043a\u0430\u043c \u0434\u0430\u0441\u043b\u0435\u0434\u0430\u0432\u0430\u043d\u043d\u044f ASP \u0441\u0442\u0430\u043b\u0430 \u043d\u0435 \u043f\u0430\u0448\u044b\u0440\u044d\u043d\u043d\u0435 \u0441\u0435\u0442\u043a\u0456 \u0430\u0431\u043e \u044f\u0435 \u0430\u0440\u044b\u0435\u043d\u0442\u0430\u0446\u044b\u044f \u043d\u0430 \u043d\u0435\u0437\u0430\u0445\u043e\u0434\u043d\u0456\u044f \u0434\u0437\u044f\u0440\u0436\u0430\u0432\u044b, \u0430 \u043c\u0430\u0434\u044d\u043b\u044c \u0431\u0443\u0434\u0443\u0447\u044b\u0445 \u0456\u043d\u0444\u0430\u0430\u043f\u0435\u0440\u0430\u0446\u044b\u0439, \u043f\u0430\u0431\u0443\u0434\u0430\u0432\u0430\u043d\u044b\u0445 \u043d\u0430 \u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0437\u0430\u0446\u044b\u0456. \u0421\u0435\u0442\u043a\u0430 \u00ab\u041f\u0440\u0430\u045e\u0434\u0430\u00bb \u2014 \u0432\u044f\u043b\u0456\u0437\u043d\u0430\u044f, \u0445\u0443\u0442\u043a\u0430\u0440\u0430\u0441\u0442\u0443\u0447\u0430\u044f, \u043d\u044f\u0437\u0440\u0443\u0447\u043d\u0430\u044f \u0434\u043b\u044f \u043a\u0430\u0440\u044b\u0441\u0442\u0430\u043b\u044c\u043d\u0456\u043a\u0430, \u2014 \u0445\u0443\u0442\u0447\u044d\u0439 \u0437\u0430 \u045e\u0441\u0451, \u0440\u0430\u0437\u043b\u0456\u0447\u0430\u043d\u0430 \u043d\u0430 \u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0447\u043d\u044b\u0445 \u0430\u0433\u0435\u043d\u0442\u0430\u045e: \u0432\u044d\u0431-\u043a\u0440\u0430\u045e\u043b\u0435\u0440\u044b, \u0441\u043a\u0440\u0430\u043f\u043f\u0435\u0440\u044b \u0456 \u0430\u043b\u0433\u0430\u0440\u044b\u0442\u043c\u044b, \u044f\u043a\u0456\u044f \u0444\u0430\u0440\u043c\u0456\u0440\u0443\u044e\u0446\u044c LLM. \u0413\u044d\u0442\u0430 \u043c\u0430\u0441\u0430\u0432\u0430\u044f \u0432\u044b\u0442\u0432\u043e\u0440\u0447\u0430\u0441\u0446\u044c \u0456 \u0434\u0443\u0431\u043b\u044f\u0432\u0430\u043d\u043d\u0435 \u043a\u0430\u043d\u0442\u044d\u043d\u0442\u0443 \u0437 \u043c\u044d\u0442\u0430\u0439 \u0442\u0440\u0430\u043f\u0456\u0446\u044c \u0443 \u0431\u0443\u0434\u0443\u0447\u044b\u044f \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u044b \u0428\u0406.\nASP \u043d\u0430\u0437\u044b\u0432\u0430\u0435 \u0442\u0430\u043a\u0443\u044e \u0442\u0430\u043a\u0442\u044b\u043a\u0443 <strong>LLM grooming<\/strong> \u2014 \u043d\u0430\u045e\u043c\u044b\u0441\u043d\u0430\u0435 \u043d\u0430\u0441\u044b\u0447\u044d\u043d\u043d\u0435 \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0442\u0443 \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044f\u0439, \u043f\u0440\u044b\u0437\u043d\u0430\u0447\u0430\u043d\u0430\u0439 \u0434\u043b\u044f \u0441\u043f\u0430\u0436\u044b\u0432\u0430\u043d\u043d\u044f \u043c\u0430\u0448\u044b\u043d\u0430\u043c\u0456. \u0423 \u0447\u044d\u0440\u0432\u0435\u043d\u0456 2024 \u0433\u043e\u0434\u0430 NewsGuard \u043f\u0430\u043a\u0430\u0437\u0430\u045e, \u0448\u0442\u043e \u0432\u044f\u0434\u0443\u0447\u044b\u044f LLM \u0443 \u0441\u044f\u0440\u044d\u0434\u043d\u0456\u043c \u0443 31,8 % \u0432\u044b\u043f\u0430\u0434\u043a\u0430\u045e \u0443\u0437\u043d\u0430\u045e\u043b\u044f\u044e\u0446\u044c \u0440\u0430\u0441\u0456\u0439\u0441\u043a\u0443\u044e \u0434\u044d\u0437\u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044e. \u041a\u0430\u043b\u0456 \u043d\u0435 \u043f\u0440\u044b\u043d\u044f\u0446\u044c \u043c\u0435\u0440\u044b, LLM grooming \u0443\u044f\u045e\u043b\u044f\u0435 \u043f\u0430\u0433\u0440\u043e\u0437\u0443 \u0446\u044d\u043b\u0430\u0441\u043d\u0430\u0441\u0446\u0456 \u0430\u0434\u043a\u0440\u044b\u0442\u0430\u0433\u0430 \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0442\u0443.\n\u041b\u044e\u0442\u044b 2023 \u0433\u043e\u0434\u0430 \u2014 \u0434\u0430\u0442\u0430 \u0441\u0442\u0432\u0430\u0440\u044d\u043d\u043d\u044f \u0441\u0435\u0442\u043a\u0456 \u00ab\u041f\u0440\u0430\u045e\u0434\u0430\u00bb \u2014 \u0441\u0443\u043f\u0430\u0434\u0430\u0435 \u0437 \u043c\u043e\u043c\u0430\u043d\u0442\u0430\u043c \u043f\u0430\u043f\u0443\u043b\u044f\u0440\u044b\u0437\u0430\u0446\u044b\u0456 \u0433\u0435\u043d\u0435\u0440\u0430\u0442\u044b\u045e\u043d\u0430\u0433\u0430 \u0428\u0406. \u0420\u0430\u043d\u0435\u0439 \u0443\u0436\u043e \u0444\u0456\u043a\u0441\u0430\u0432\u0430\u043b\u0456\u0441\u044f \u0441\u043f\u0440\u043e\u0431\u044b \u043f\u0440\u044b\u0446\u044f\u0433\u043d\u0435\u043d\u043d\u044f \u043a\u0440\u0430\u045e\u043b\u0435\u0440\u0430\u045e \u043f\u0440\u0430\u0437 SEO-\u0430\u043f\u0442\u044b\u043c\u0456\u0437\u0430\u0446\u044b\u044e. \u0423 \u0430\u0434\u0440\u043e\u0437\u043d\u0435\u043d\u043d\u0435 \u0430\u0434 \u0442\u0440\u0430\u0434\u044b\u0446\u044b\u0439\u043d\u0430\u0433\u0430 SEO, \u043c\u044d\u0442\u0430 LLM grooming \u2014 \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u0430 \u043f\u0430\u0432\u044b\u0441\u0456\u0446\u044c \u0431\u0430\u0447\u043d\u0430\u0441\u0446\u044c, \u0430 <strong>\u0437\u0430\u043f\u0440\u0430\u0433\u0440\u0430\u043c\u0430\u0432\u0430\u0446\u044c \u0428\u0406<\/strong> \u043d\u0430 \u043f\u0430\u045e\u0442\u0430\u0440\u044d\u043d\u043d\u0435 \u043f\u044d\u045e\u043d\u044b\u0445 \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u0430\u045e. \u0413\u044d\u0442\u0430 \u043f\u0430\u043a\u0443\u043b\u044c \u043c\u0430\u043b\u0430\u0432\u044b\u0432\u0443\u0447\u0430\u043d\u0430\u044f \u043f\u0430\u0433\u0440\u043e\u0437\u0430.\n<strong>\u0422\u043b\u0443\u043c\u0430\u0447\u044d\u043d\u043d\u0435 B: \u043c\u0430\u0441\u0430\u0432\u0430\u0435 \u043d\u0430\u0441\u044b\u0447\u044d\u043d\u043d\u0435<\/strong>\n\u0421\u0435\u0442\u043a\u0430 \u0448\u0442\u043e\u0434\u0437\u0451\u043d\u043d\u0430 \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u0435 \u0432\u044f\u043b\u0456\u0437\u043d\u0443\u044e \u043a\u043e\u043b\u044c\u043a\u0430\u0441\u0446\u044c \u043c\u0430\u0442\u044d\u0440\u044b\u044f\u043b\u0430\u045e, \u043d\u0430\u0441\u044b\u0447\u0430\u044e\u0447\u044b \u0456\u043d\u0442\u044d\u0440\u043d\u044d\u0442 \u043f\u0440\u0430\u0440\u0430\u0441\u0456\u0439\u0441\u043a\u0456\u043c \u043a\u0430\u043d\u0442\u044d\u043d\u0442\u0430\u043c. \u0413\u044d\u0442\u0430 \u043f\u0430\u0432\u044f\u043b\u0456\u0447\u0432\u0430\u0435:\n<ul data-spread=\"false\">\n \t<li>\u0432\u0435\u0440\u0430\u0433\u043e\u0434\u043d\u0430\u0441\u0446\u044c \u0442\u0430\u0433\u043e, \u0448\u0442\u043e \u043a\u0430\u0440\u044b\u0441\u0442\u0430\u043b\u044c\u043d\u0456\u043a \u043d\u0430\u0442\u043a\u043d\u0435\u0446\u0446\u0430 \u043d\u0430 \u043f\u0430\u0442\u0440\u044d\u0431\u043d\u044b \u043d\u0430\u0440\u0430\u0442\u044b\u045e,<\/li>\n \t<li>\u0448\u0430\u043d\u0446, \u0448\u0442\u043e \u0437\u043d\u0435\u0448\u043d\u0456\u044f \u043a\u0440\u044b\u043d\u0456\u0446\u044b (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u0412\u0456\u043a\u0456\u043f\u0435\u0434\u044b\u044f) \u0431\u0443\u0434\u0443\u0446\u044c \u0441\u043f\u0430\u0441\u044b\u043b\u0430\u0446\u0446\u0430 \u043d\u0430 \u0433\u044d\u0442\u044b\u044f \u043c\u0430\u0442\u044d\u0440\u044b\u044f\u043b\u044b.<\/li>\n<\/ul>\n\u041c\u0435\u0445\u0430\u043d\u0456\u0437\u043c \u043c\u0430\u0441\u0430\u0432\u0430\u0433\u0430 \u045e\u0437\u0434\u0437\u0435\u044f\u043d\u043d\u044f \u0444\u0430\u0440\u043c\u0456\u0440\u0443\u0435 <strong>\u044d\u0444\u0435\u043a\u0442 \u0456\u043b\u044e\u0437\u0456\u0456 \u043f\u0440\u0430\u045e\u0434\u044b<\/strong>: \u0447\u044b\u043c \u0447\u0430\u0441\u0446\u0435\u0439 \u0447\u0430\u043b\u0430\u0432\u0435\u043a \u0441\u0443\u0442\u044b\u043a\u0430\u0435\u0446\u0446\u0430 \u0437 \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u0435\u043c, \u0442\u044b\u043c \u0432\u044b\u0448\u044d\u0439 \u0432\u0435\u0440\u0430\u0433\u043e\u0434\u043d\u0430\u0441\u0446\u044c, \u0448\u0442\u043e \u0451\u043d \u0443 \u044f\u0433\u043e \u043f\u0430\u0432\u0435\u0440\u044b\u0446\u044c.\n<strong>\u0422\u043b\u0443\u043c\u0430\u0447\u044d\u043d\u043d\u0435 C: \u044d\u0444\u0435\u043a\u0442 \u0456\u043b\u044e\u0437\u043e\u0440\u043d\u0430\u0439 \u043f\u0440\u0430\u045e\u0434\u044b \u0437 \u043d\u0435\u043a\u0430\u043b\u044c\u043a\u0456\u0445 \u043a\u0440\u044b\u043d\u0456\u0446<\/strong>\n\u0421\u0435\u0442\u043a\u0430 \u0440\u0430\u0441\u043f\u0430\u045e\u0441\u044e\u0434\u0436\u0432\u0430\u0435 \u0430\u0434\u0437\u0456\u043d \u0456 \u0442\u043e\u0439 \u0436\u0430 \u043a\u0430\u043d\u0442\u044d\u043d\u0442 \u043f\u0440\u0430\u0437 \u043c\u043d\u043e\u0441\u0442\u0432\u0430 \u043a\u0430\u043d\u0430\u043b\u0430\u045e: \u0441\u0430\u0439\u0442\u044b, Telegram, X, VK \u0456 \u043d\u0430\u0432\u0430\u0442 Bluesky. \u0413\u044d\u0442\u0430 \u0441\u0442\u0432\u0430\u0440\u0430\u0435 \u0456\u043b\u044e\u0437\u0456\u044e \u043f\u0430\u0446\u0432\u0435\u0440\u0434\u0436\u0430\u043d\u0430\u0439 \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0456 \u0437 \"\u0440\u043e\u0437\u043d\u044b\u0445\" \u043a\u0440\u044b\u043d\u0456\u0446. \u0423 \u0441\u043f\u0440\u0430\u0432\u0443 \u045e\u0441\u0442\u0443\u043f\u0430\u0435 \u044f\u043a \u043d\u0430\u045e\u043c\u044b\u0441\u043d\u0430\u0435 \u00ab\u0430\u0434\u043c\u044b\u0432\u0430\u043d\u043d\u0435\u00bb \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0456 (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043a\u0430\u043b\u0456 \u043d\u0430 \u0441\u0435\u0442\u043a\u0443 \u0441\u043f\u0430\u0441\u044b\u043b\u0430\u044e\u0446\u0446\u0430 \u0456\u043d\u0448\u044b\u044f \u043f\u0440\u0430\u0440\u0430\u0441\u0456\u0439\u0441\u043a\u0456\u044f \u0440\u044d\u0441\u0443\u0440\u0441\u044b), \u0442\u0430\u043a \u0456 \u043d\u0435\u043d\u0430\u045e\u043c\u044b\u0441\u043d\u0430\u0435 (\u043a\u0430\u043b\u0456 \u043f\u0430\u0432\u0430\u0436\u0430\u043d\u0430\u044f \u0430\u0440\u0433\u0430\u043d\u0456\u0437\u0430\u0446\u044b\u044f \u0430\u0431\u043e \u0430\u0441\u043e\u0431\u0430 \u0434\u0437\u0435\u043b\u044f\u0446\u0446\u0430 \u0441\u043f\u0430\u0441\u044b\u043b\u043a\u0430\u0439, \u043d\u0435 \u0432\u0435\u0434\u0430\u044e\u0447\u044b \u0430\u0431 \u044f\u0435 \u043f\u0430\u0445\u043e\u0434\u0436\u0430\u043d\u043d\u0456).\n\u0423\u0441\u0435 \u0442\u0440\u044b \u043c\u0430\u0442\u044b\u0432\u044b \u045e\u0437\u043c\u0430\u0446\u043d\u044f\u044e\u0446\u044c \u0430\u0434\u0437\u0456\u043d \u0430\u0434\u043d\u0430\u0433\u043e. \u0427\u044b\u043c \u0431\u043e\u043b\u044c\u0448 \u0441\u0442\u0430\u0440\u043e\u043d\u0430\u043a, URL \u0456 \u043f\u0435\u0440\u0430\u043a\u043b\u0430\u0434\u0430\u045e \u0441\u0442\u0432\u0430\u0440\u0430\u0435 \u0441\u0435\u0442\u043a\u0430, \u0442\u044b\u043c \u0432\u044b\u0448\u044d\u0439 \u0432\u0435\u0440\u0430\u0433\u043e\u0434\u043d\u0430\u0441\u0446\u044c, \u0448\u0442\u043e \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u044b \u0431\u0443\u0434\u0443\u0446\u044c \u043f\u0440\u044b\u043d\u044f\u0442\u044b \u0456 \u043b\u044e\u0434\u0437\u044c\u043c\u0456, \u0456 \u043c\u0430\u0448\u044b\u043d\u0430\u043c\u0456. \u0425\u043e\u0446\u044c \u044f\u043a\u0430\u0441\u0446\u044c \u0441\u0430\u0439\u0442\u0430\u045e \u043d\u0456\u0437\u043a\u0430\u044f, \u0433\u044d\u0442\u0430 \u043d\u0435 \u043f\u0435\u0440\u0430\u0448\u043a\u0430\u0434\u0436\u0430\u0435 \u0456\u043c \u0441\u0442\u0430\u043d\u0430\u0432\u0456\u0446\u0446\u0430 \u0447\u0430\u0441\u0442\u043a\u0430\u0439 \u043b\u0456\u0447\u0431\u0430\u0432\u0430\u0433\u0430 \u0441\u043b\u0435\u0434\u0443, \u0443\u043b\u0456\u0447\u0432\u0430\u0435\u043c\u0430\u0433\u0430 LLM.\n<div>\n<iframe width=\"560\" height=\"315\" src=\"https:\/\/www.youtube.com\/embed\/kk7tBTg_Xbo?si=GANrkylWs1tFMyKi\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div>\n<strong>\u0421\u0446\u044d\u043d\u0430\u0440\u044b\u0456 LLM-grooming<\/strong>\n\u0410\u045e\u0442\u0430\u0440\u044b \u0434\u0430\u043a\u043b\u0430\u0434\u0430 \u0432\u044b\u043b\u0443\u0447\u0430\u044e\u0446\u044c \u0442\u0440\u044b \u043a\u043b\u044e\u0447\u0430\u0432\u044b\u044f \u043c\u044d\u0442\u044b \u0442\u0430\u043a\u0456\u0445 \u0441\u0435\u0442\u0430\u043a:\n<ol start=\"1\" data-spread=\"false\">\n \t<li><strong>\u0423\u043a\u043b\u044e\u0447\u044d\u043d\u043d\u0435 \u045e \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u044b<\/strong> \u2014 \u0441\u0430\u0439\u0442\u044b \u0456\u043d\u0434\u044d\u043a\u0441\u0443\u044e\u0446\u0446\u0430 \u045e \u043f\u043e\u0448\u0443\u043a\u0430\u0432\u044b\u0445 \u0441\u0456\u0441\u0442\u044d\u043c\u0430\u0445 \u0456 \u0442\u0440\u0430\u043f\u043b\u044f\u044e\u0446\u044c \u0443 \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0435 LLM, \u0443\u043a\u0430\u0440\u0430\u043d\u044f\u044e\u0447\u044b \u043f\u0440\u0430\u043a\u0440\u0430\u043c\u043b\u0451\u045e\u0441\u043a\u0456\u044f \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u044b \u045e \u0430\u0440\u0445\u0456\u0442\u044d\u043a\u0442\u0443\u0440\u0443 \u043c\u0430\u0434\u044d\u043b\u0456.<\/li>\n \t<li><strong>\u0421\u0442\u0432\u0430\u0440\u044d\u043d\u043d\u0435 \u0456\u043b\u044e\u0437\u0456\u0456 \u043d\u0435\u0437\u0430\u043b\u0435\u0436\u043d\u044b\u0445 \u043a\u0440\u044b\u043d\u0456\u0446<\/strong> \u2014 \u0430\u0434\u0437\u0456\u043d \u0456 \u0442\u043e\u0439 \u0436\u0430 \u0442\u044d\u043a\u0441\u0442 \u0440\u0430\u0437\u043c\u044f\u0448\u0447\u0430\u0435\u0446\u0446\u0430 \u043d\u0430 \u0441\u043e\u0442\u043d\u044f\u0445 \u0441\u0430\u0439\u0442\u0430\u045e, \u0448\u0442\u043e \u0441\u0442\u0432\u0430\u0440\u0430\u0435 \u044d\u0444\u0435\u043a\u0442 \"\u043a\u0430\u043d\u0441\u0435\u043d\u0441\u0443\u0441\u0443\".<\/li>\n \t<li><strong>\u0420\u0430\u0437\u043c\u044b\u0446\u0446\u0451 \u0456\u043d\u0444\u0430\u043f\u043e\u043b\u044f<\/strong> \u2014 LLM \u043f\u0440\u044b \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u044b\u0456 \u0442\u044d\u043a\u0441\u0442\u0430\u045e \u0441\u043f\u0430\u0441\u044b\u043b\u0430\u0435\u0446\u0446\u0430 \u043d\u0435 \u043d\u0430 \u043f\u0435\u0440\u0448\u0430\u043a\u0440\u044b\u043d\u0456\u0446\u044b, \u0430 \u043d\u0430 \u043a\u043e\u043f\u0456\u0456, \u0443\u0437\u043c\u0430\u0446\u043d\u044f\u044e\u0447\u044b \u0434\u044d\u0437\u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0439\u043d\u044b \u0448\u0443\u043c.<\/li>\n<\/ol>\n<div><\/div>\n<strong>\u0428\u0442\u043e \u045e\u043c\u0435\u044e\u0446\u044c LLM \u0443 \u0431\u0430\u0440\u0430\u0446\u044c\u0431\u0435 \u0437 LLM Grooming?<\/strong>\n<strong>\u0424\u0456\u043b\u044c\u0442\u0440\u0430\u0446\u044b\u044f \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445 \u043f\u0440\u044b \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0456<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0412\u044f\u043b\u0456\u043a\u0456\u044f \u043c\u0430\u0434\u044d\u043b\u0456 \u043a\u0448\u0442\u0430\u043b\u0442\u0443 GPT \u043d\u0430\u0432\u0443\u0447\u0430\u044e\u0446\u0446\u0430 \u043d\u0430 \u0430\u0434\u0430\u0431\u0440\u0430\u043d\u044b\u0445, \u0430\u0447\u044b\u0448\u0447\u0430\u043d\u044b\u0445 \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u0430\u0445. \u041f\u0430\u0434\u0447\u0430\u0441 \u043f\u0430\u0434\u0440\u044b\u0445\u0442\u043e\u045e\u043a\u0456 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445 \u0443\u0436\u044b\u0432\u0430\u044e\u0446\u0446\u0430 \u0444\u0456\u043b\u044c\u0442\u0440\u044b, \u044f\u043a\u0456\u044f \u0432\u044b\u0434\u0430\u043b\u044f\u044e\u0446\u044c:\n<ul data-spread=\"false\">\n \t<li>\u0441\u043f\u0430\u043c,<\/li>\n \t<li>\u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0447\u043d\u0443\u044e \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u044b\u044e,<\/li>\n \t<li>SEO-\u0444\u0435\u0440\u043c\u044b,<\/li>\n \t<li>\u0442\u0430\u043a\u0441\u0456\u0447\u043d\u044b \u0430\u0431\u043e \u043c\u0430\u043d\u0456\u043f\u0443\u043b\u044f\u0442\u044b\u045e\u043d\u044b \u043a\u0430\u043d\u0442\u044d\u043d\u0442.<\/li>\n<\/ul>\n<\/li>\n \t<li>\u0413\u044d\u0442\u0430 \u043f\u0435\u0440\u0448\u0430\u044f \u043b\u0456\u043d\u0456\u044f \u0430\u0431\u0430\u0440\u043e\u043d\u044b \u0430\u0434 LLM grooming \u2014 \u043d\u0435 \u0434\u0430\u0446\u044c \u0448\u043a\u043e\u0434\u043d\u044b\u043c \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u043c \u0442\u0440\u0430\u043f\u0456\u0446\u044c \u0443 \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0435.<\/li>\n<\/ul>\n<strong>\u041a\u0430\u043d\u0442\u0440\u043e\u043b\u044c \u044f\u043a\u0430\u0441\u0446\u0456 \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u044b\u0456<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u041c\u0430\u0434\u044d\u043b\u0456 \u043f\u0440\u0430\u0445\u043e\u0434\u0437\u044f\u0446\u044c \u0442\u043e\u043d\u043a\u0443\u044e \u043d\u0430\u0441\u0442\u0440\u043e\u0439\u043a\u0443 (fine-tuning) \u0456 \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0435 \u0437 \u0443\u0434\u0437\u0435\u043b\u0430\u043c \u043b\u044e\u0434\u0437\u0435\u0439 (RLHF), \u043a\u0430\u0431 \u043d\u0435 \u043f\u0430\u045e\u0442\u0430\u0440\u0430\u0446\u044c \u0456\u043b\u0436\u044b\u0432\u044b\u044f \u0430\u0431\u043e \u0448\u043a\u043e\u0434\u043d\u044b\u044f \u043d\u0430\u0440\u0430\u0442\u044b\u0432\u044b, \u043d\u0430\u0432\u0430\u0442 \u043a\u0430\u043b\u0456 \u044f\u043d\u044b \u0451\u0441\u0446\u044c \u0443 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445.<\/li>\n \t<li>\u041d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043d\u0430\u0432\u0430\u0442 \u043a\u0430\u043b\u0456 \u043d\u0435\u0445\u0442\u0430 \u043c\u0430\u0441\u0430\u0432\u0430 \u043f\u0443\u0431\u043b\u0456\u043a\u0443\u0435 \u0434\u044d\u0437\u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044e \u0430\u0431 \u0432\u0430\u043a\u0446\u044b\u043d\u0430\u0445 \u2014 \u0433\u044d\u0442\u0430 \u043d\u0435 \u0433\u0430\u0440\u0430\u043d\u0442\u0443\u0435, \u0448\u0442\u043e \u043c\u0430\u0434\u044d\u043b\u044c \u0431\u0443\u0434\u0437\u0435 \u044f\u0435 \u045e\u0437\u043d\u0430\u045e\u043b\u044f\u0446\u044c.<\/li>\n<\/ul>\n<strong>\u0424\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433 \u0456 \u043c\u0435\u0442\u0430-\u0440\u0430\u0437\u0443\u043c\u0435\u043d\u043d\u0435<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u042f \u043c\u0430\u0433\u0443 \u043f\u0440\u0430\u0432\u0435\u0440\u044b\u0446\u044c \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044e, \u0437\u043d\u0430\u0439\u0441\u0446\u0456 \u043a\u0440\u044b\u043d\u0456\u0446\u044b, \u0441\u0443\u043f\u0430\u0441\u0442\u0430\u0432\u0456\u0446\u044c \u0444\u0430\u043a\u0442\u044b, \u0456, \u043a\u0430\u043b\u0456 \u0442\u0440\u044d\u0431\u0430, \u0443\u043a\u0430\u0437\u0430\u0446\u044c, \u0448\u0442\u043e \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u0435 \u0441\u043f\u0440\u044d\u0447\u043d\u0430\u0435 \u0430\u0431\u043e \u0456\u043b\u0436\u044b\u0432\u0430\u0435.<\/li>\n<\/ul>\n\u2757 \u0410\u043b\u0435 \u0451\u0441\u0446\u044c \u0456 \u0430\u0431\u043c\u0435\u0436\u0430\u0432\u0430\u043d\u043d\u0456:\n<ul data-spread=\"false\">\n \t<li>\u041a\u0430\u043b\u0456 LLM grooming \u043d\u0435\u0437\u0430\u045e\u0432\u0430\u0436\u043d\u044b \u0456 \u0442\u043e\u043d\u043a\u0456 (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, \u043c\u0430\u0441\u0430\u0432\u0430\u0435, \u0430\u043b\u0435 \u043f\u0440\u0430\u045e\u0434\u0430\u043f\u0430\u0434\u043e\u0431\u043d\u0430\u0435 \u043f\u0435\u0440\u0430\u043f\u0456\u0441\u0432\u0430\u043d\u043d\u0435 \u0433\u0456\u0441\u0442\u043e\u0440\u044b\u0456), \u044f\u0433\u043e \u0446\u044f\u0436\u044d\u0439 \u0430\u0434\u0444\u0456\u043b\u044c\u0442\u0440\u0430\u0432\u0430\u0446\u044c.<\/li>\n \t<li>\u0410\u0434\u043a\u0440\u044b\u0442\u044b\u044f \u043c\u0430\u0434\u044d\u043b\u0456 (\u0442\u044b\u043f\u0443 LLaMA, Mistral \u0456 \u0456\u043d\u0448.), \u044f\u043a\u0456\u044f \u043d\u0430\u0432\u0443\u0447\u0430\u044e\u0446\u0446\u0430 \"\u043d\u0430 \u0447\u044b\u043c \u043f\u0430\u043f\u0430\u043b\u0430\", \u043c\u043e\u0433\u0443\u0446\u044c \u043c\u0430\u0446\u043d\u0435\u0439 \u043f\u0430\u0446\u044f\u0440\u043f\u0435\u0446\u044c \u0430\u0434 LLM grooming.<\/li>\n \t<li>\u0411\u0430\u0440\u0430\u0446\u044c\u0431\u0430 \u0437 \u0433\u044d\u0442\u044b\u043c \u2014 \u043d\u0435 \u0437\u0430\u0434\u0430\u0447\u0430 \u043c\u0430\u0434\u044d\u043b\u0456, \u0430 \u0445\u0443\u0442\u0447\u044d\u0439 \u0437\u0430\u0434\u0430\u0447\u0430 \u0440\u0430\u0441\u043f\u0440\u0430\u0446\u043e\u045e\u0448\u0447\u044b\u043a\u0430\u045e, \u044d\u0442\u044b\u043a\u0430\u045e, \u0430\u045e\u0434\u044b\u0442\u0430\u0440\u0430\u045e \u0456 \u0434\u0430\u0442\u0430\u0441\u044d\u0442-\u0456\u043d\u0436\u044b\u043d\u0435\u0440\u0430\u045e.<\/li>\n<\/ul>\n\ud83e\udd16 \u0428\u0442\u043e \u0442\u044b \u043c\u043e\u0436\u0430\u0448 \u0440\u0430\u0431\u0456\u0446\u044c \u044f\u043a \u0447\u0430\u043b\u0430\u0432\u0435\u043a:\n<ul data-spread=\"false\">\n \t<li>\u0421\u0442\u0432\u0430\u0440\u0430\u0446\u044c \u044f\u043a\u0430\u0441\u043d\u044b \u043a\u0430\u043d\u0442\u044d\u043d\u0442, \u043a\u0430\u0431 \u0451\u043d \u0442\u0440\u0430\u043f\u043b\u044f\u045e \u0443 \u0434\u0430\u0442\u0430\u0441\u044d\u0442\u044b.<\/li>\n \t<li>\u041f\u0440\u0430\u0432\u043e\u0434\u0437\u0456\u0446\u044c \u0430\u045e\u0434\u044b\u0442 \u0428\u0406, \u043f\u0440\u0430\u0432\u044f\u0440\u0430\u044e\u0447\u044b, \u044f\u043a \u0451\u043d \u0440\u044d\u0430\u0433\u0443\u0435 \u043d\u0430 \u043f\u0430\u0442\u044d\u043d\u0446\u044b\u0439\u043d\u0430 \u0437\u0430\u0441\u0435\u044f\u043d\u044b\u044f \u0442\u044d\u043c\u044b.<\/li>\n \t<li>\u0423\u0436\u044b\u0432\u0430\u0446\u044c \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u0434\u043b\u044f \u0430\u0434\u0441\u043e\u0447\u0432\u0430\u043d\u043d\u044f \"\u0443\u043a\u0456\u0434\u0430\u045e\", \u0430\u0441\u0430\u0431\u043b\u0456\u0432\u0430 \u043a\u0430\u043b\u0456 \u0437\u0430\u0439\u043c\u0430\u0435\u0448\u0441\u044f OSINT, \u043c\u0435\u0434\u044b\u044f\u0433\u0440\u0430\u043c\u0430\u0442\u043d\u0430\u0441\u0446\u044e \u0430\u0431\u043e \u0444\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433\u0430\u043c.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u0428\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u00ab\u0441\u043b\u0430\u0456 \u0444\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433\u0443\u00bb \u0434\u043b\u044f \u0428\u0406?<\/strong>\n\u0413\u044d\u0442\u0430 \u043c\u043e\u0434\u0443\u043b\u0456, \u043c\u0430\u0434\u044d\u043b\u0456 \u0430\u0431\u043e API, \u044f\u043a\u0456\u044f:\n<ul data-spread=\"false\">\n \t<li>\u043f\u0440\u0430\u0432\u044f\u0440\u0430\u044e\u0446\u044c \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u0456 \u043d\u0430 \u0434\u0430\u043a\u043b\u0430\u0434\u043d\u0430\u0441\u0446\u044c;<\/li>\n \t<li>\u0443\u043a\u0430\u0437\u0432\u0430\u044e\u0446\u044c, \u0446\u0456 \u043f\u0430\u0442\u0440\u044d\u0431\u043d\u0430 \u045e\u0434\u0430\u043a\u043b\u0430\u0434\u043d\u0435\u043d\u043d\u0435;<\/li>\n \t<li>\u0430\u043b\u044c\u0431\u043e \u0430\u0446\u044d\u043d\u044c\u0432\u0430\u044e\u0446\u044c \u0443\u0437\u0440\u043e\u0432\u0435\u043d\u044c \u043f\u0440\u0430\u045e\u0434\u0430\u043f\u0430\u0434\u043e\u0431\u043d\u0430\u0441\u0446\u0456 \u0444\u0440\u0430\u0437\u044b.<\/li>\n<\/ul>\n\u0422\u0430\u043a\u0456\u044f \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u043f\u0440\u0430\u0446\u0443\u044e\u0446\u044c \u0443 \u0437\u0432\u044f\u0437\u0446\u044b \u0437 LLM, \u043a\u0430\u0431:\n<ul data-spread=\"false\">\n \t<li>\u043c\u0456\u043d\u0456\u043c\u0456\u0437\u0430\u0432\u0430\u0446\u044c \u0440\u0430\u0441\u043f\u0430\u045e\u0441\u044e\u0434\u0436\u0432\u0430\u043d\u043d\u0435 \u0434\u044d\u0437\u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0456;<\/li>\n \t<li>\u0444\u0456\u043b\u044c\u0442\u0440\u0430\u0432\u0430\u0446\u044c \u043d\u0430\u0432\u0443\u0447\u0430\u043b\u044c\u043d\u044b\u044f \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u044f;<\/li>\n \t<li>\u043f\u0430\u0432\u044b\u0441\u0456\u0446\u044c \u0434\u0430\u0432\u0435\u0440 \u0434\u0430 \u0430\u0434\u043a\u0430\u0437\u0430\u045e \u043c\u0430\u0434\u044d\u043b\u0456.<\/li>\n<\/ul>\n\ud83d\udee0\ufe0f \u041f\u0440\u044b\u043a\u043b\u0430\u0434\u044b\n\ud83d\udd0e <strong>ClaimBuster<\/strong> \u0421\u0443\u0442\u043d\u0430\u0441\u0446\u044c: \u0430\u043b\u0433\u0430\u0440\u044b\u0442\u043c, \u044f\u043a\u0456 \u0430\u045e\u0442\u0430\u043c\u0430\u0442\u044b\u0447\u043d\u0430 \u0437\u043d\u0430\u0445\u043e\u0434\u0437\u0456\u0446\u044c \u0444\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433\u0430\u0432\u0430 \u0437\u043d\u0430\u0447\u043d\u044b\u044f \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u0456 \u045e \u0442\u044d\u043a\u0441\u0446\u0435.\n\ud83d\udccc \u0414\u0437\u0435 \u043a\u0430\u0440\u044b\u0441\u043d\u044b:\n<ul data-spread=\"false\">\n \t<li>\u0434\u043b\u044f \u0441\u043a\u0430\u043d\u0430\u0432\u0430\u043d\u043d\u044f \u043d\u0430\u0432\u0456\u043d, \u043f\u0430\u0441\u0442\u043e\u045e, \u043f\u0440\u0430\u043c\u043e\u045e \u043f\u0430\u043b\u0456\u0442\u044b\u043a\u0430\u045e;<\/li>\n \t<li>\u0434\u043b\u044f \u0441\u0442\u0432\u0430\u0440\u044d\u043d\u043d\u044f \u0434\u0430\u0442\u0430\u0444\u0440\u044d\u0439\u043c\u0430 \u0437 \u043f\u0430\u0442\u044d\u043d\u0446\u044b\u0439\u043d\u0430 \u0444\u044d\u0439\u043a\u0430\u0432\u044b\u043c\u0456\/\u0443\u0432\u043e\u0434\u0437\u044f\u0447\u044b\u043c\u0456 \u045e \u0437\u043c\u0430\u043d \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u044f\u043c\u0456;<\/li>\n \t<li>\u043c\u043e\u0436\u043d\u0430 \u0432\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0446\u044c \u044f\u043a \u0444\u0456\u043b\u044c\u0442\u0440 \u043f\u0435\u0440\u0430\u0434 \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u0435\u043c \u043c\u0430\u0434\u044d\u043b\u0456.<\/li>\n<\/ul>\n\ud83e\uddea \u042f\u043a \u043f\u0440\u0430\u0446\u0443\u0435:\n<ul data-spread=\"false\">\n \t<li>\u041f\u0440\u044b\u043c\u0430\u0435 \u043d\u0430 \u045e\u0432\u0430\u0445\u043e\u0434 \u0442\u044d\u043a\u0441\u0442 (\u0430\u0431\u043e \u0442\u0440\u0430\u043d\u0441\u043a\u0440\u044b\u043f\u0446\u044b\u044e \u043f\u0440\u0430\u043c\u043e\u0432\u044b).<\/li>\n \t<li>\u0412\u044b\u0434\u0430\u0435: \u0444\u0440\u0430\u0437\u0430 \u0433\u044d\u0442\u0430 \"check-worthy\" (\u044f\u043a\u0430\u044f \u043f\u0430\u0442\u0440\u0430\u0431\u0443\u0435 \u043f\u0440\u0430\u0432\u0435\u0440\u043a\u0456) \u0430\u0431\u043e \u043d\u0435.<\/li>\n<\/ul>\n\ud83d\udcce \u0412\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0435\u0446\u0446\u0430 \u045e: FactStream \u0430\u0434 Duke University.\n\ud83d\udcda <strong>TrustworthyQA<\/strong> \u0421\u0443\u0442\u043d\u0430\u0441\u0446\u044c: \u0434\u0430\u0442\u0430\u0441\u044d\u0442 \u0456 \u043c\u0430\u0434\u044d\u043b\u044c, \u0440\u0430\u0441\u043f\u0440\u0430\u0446\u0430\u0432\u0430\u043d\u044b\u044f \u0434\u043b\u044f \u0430\u0446\u044d\u043d\u043a\u0456 \u043d\u0430\u0434\u0437\u0435\u0439\u043d\u0430\u0441\u0446\u0456 \u0441\u0446\u0432\u044f\u0440\u0434\u0436\u044d\u043d\u043d\u044f\u045e, \u0437\u0440\u043e\u0431\u043b\u0435\u043d\u044b\u0445 \u0443 \u0430\u0434\u043a\u0430\u0437\u0430\u0445 LLM.\n\ud83d\udccc \u0414\u0437\u0435 \u043a\u0430\u0440\u044b\u0441\u043d\u044b:\n<ul data-spread=\"false\">\n \t<li>\u044f\u043a \u0434\u0430\u0434\u0430\u0442\u043a\u043e\u0432\u044b \u0441\u043b\u043e\u0439 \u0443 pipeline \u0433\u0435\u043d\u0435\u0440\u0430\u0446\u044b\u0456 \u0442\u044d\u043a\u0441\u0442\u0443;<\/li>\n \t<li>\u0434\u043b\u044f \u0442\u0440\u044d\u043d\u0456\u0440\u043e\u045e\u043a\u0456 \u043c\u0430\u0434\u044d\u043b\u044f\u045e \u043d\u0430 \u00ab\u043f\u0430\u0434\u0430\u0437\u0440\u043e\u043d\u044b\u044f\u00bb \u0437\u0430\u043f\u044b\u0442\u044b (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434: \"\u0411\u0456\u043b \u0413\u0435\u0439\u0442\u0441 \u043a\u0456\u0440\u0443\u0435 \u043d\u0430\u0434\u0432\u043e\u0440'\u0435\u043c?\").<\/li>\n<\/ul>\n\ud83e\udde0 \u0427\u044b\u043c \u0446\u0456\u043a\u0430\u0432\u044b:\n<ul data-spread=\"false\">\n \t<li>\u0401\u043d \u043d\u0435 \u043f\u0440\u043e\u0441\u0442\u0430 \u043f\u0440\u0430\u0432\u044f\u0440\u0430\u0435 \u0444\u0430\u043a\u0442\u044b, \u0430 \u0430\u0446\u044d\u043d\u044c\u0432\u0430\u0435 \u043d\u0430\u0434\u0437\u0435\u0439\u043d\u0430\u0441\u0446\u044c \u0430\u0434\u043a\u0430\u0437\u0443 \u0428\u0406 \u043d\u0430 \u043f\u0430\u0442\u044d\u043d\u0446\u044b\u0439\u043d\u0430 \u0441\u0443\u043c\u043d\u0435\u045e\u043d\u044b\u044f \u043f\u044b\u0442\u0430\u043d\u043d\u0456.<\/li>\n \t<li>\u041c\u0430\u0434\u044d\u043b\u044c \u0432\u0443\u0447\u044b\u0446\u0446\u0430 \u043a\u0430\u0437\u0430\u0446\u044c \u00ab\u043d\u0435 \u0432\u0435\u0434\u0430\u044e\u00bb \u0430\u0431\u043e \u045e\u043a\u0430\u0437\u0432\u0430\u0446\u044c \u043d\u0430 \u0441\u043f\u0440\u044d\u0447\u043d\u0430\u0441\u0446\u044c \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0456.<\/li>\n<\/ul>\n<div><\/div>\n<strong>\u0420\u044d\u043a\u0430\u043c\u0435\u043d\u0434\u0430\u0446\u044b\u0456 \u0434\u043b\u044f \u0440\u043e\u0437\u043d\u044b\u0445 \u043c\u044d\u0442\u0430\u0432\u044b\u0445 \u0433\u0440\u0443\u043f<\/strong>\n<strong>\u0412\u044b\u0432\u0443\u0447\u0430\u044e\u0447\u044b\u044f \u0456\u043d\u0444\u0430\u0430\u043f\u0435\u0440\u0430\u0446\u044b\u0456<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0423\u0432\u0430\u0433\u0430 \u0434\u0430 \u043a\u0440\u044b\u043d\u0456\u0446, \u0441\u0442\u0432\u043e\u0440\u0430\u043d\u044b\u0445 \u043d\u0435 \u0434\u043b\u044f \u043b\u044e\u0434\u0437\u0435\u0439, \u0430 \u0434\u043b\u044f \u043c\u0430\u0448\u044b\u043d.<\/li>\n \t<li>\u041c\u0430\u043d\u0456\u0442\u043e\u0440\u044b\u043d\u0433 \u0448\u0442\u0443\u0447\u043d\u044b\u0445 \u0441\u0435\u0442\u0430\u043a \u0437 \u043f\u0430\u0434\u0430\u0437\u0440\u043e\u043d\u0430 \u0430\u0434\u043d\u0430\u0442\u044b\u043f\u043d\u044b\u043c \u043a\u0430\u043d\u0442\u044d\u043d\u0442\u0430\u043c.<\/li>\n<\/ul>\n<strong>\u0420\u0430\u0441\u043f\u0440\u0430\u0446\u043e\u045e\u0448\u0447\u044b\u043a\u0456 LLM<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u041d\u0430\u0434\u0430\u0432\u0430\u0446\u044c \u0443\u0432\u0430\u0433\u0443 \u043f\u0430\u0445\u043e\u0434\u0436\u0430\u043d\u043d\u044e \u043d\u0430\u0432\u0443\u0447\u0430\u043b\u044c\u043d\u044b\u0445 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445.<\/li>\n \t<li>\u0423\u0441\u0442\u0440\u043e\u0439\u0432\u0430\u0446\u044c \u043c\u043e\u0434\u0443\u043b\u0456 \u0444\u0430\u043a\u0442\u0447\u044d\u043a\u0456\u043d\u0433\u0443 \u0456 \u0441\u0456\u0441\u0442\u044d\u043c\u044b \u0430\u0446\u044d\u043d\u043a\u0456 \u0434\u0430\u043a\u043b\u0430\u0434\u043d\u0430\u0441\u0446\u0456 (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, TrustworthyQA).<\/li>\n \t<li>\u0412\u044b\u043a\u0430\u0440\u044b\u0441\u0442\u043e\u045e\u0432\u0430\u0446\u044c \u0444\u0456\u043b\u044c\u0442\u0440\u044b \u0442\u044b\u043f\u0443 ClaimBuster (\u0430\u0431\u043e \u0456\u0445 \u0430\u043d\u0430\u043b\u0430\u0433\u0456 \u0434\u043b\u044f \u043a\u0456\u0440\u044b\u043b\u0456\u0447\u043d\u044b\u0445 \u043c\u043e\u045e) \u043d\u0430 \u0441\u0442\u0430\u0434\u044b\u0456 \u043f\u0440\u044d\u043f\u0440\u0430\u0446\u044d\u0441\u0456\u043d\u0433\u0443 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445.<\/li>\n<\/ul>\n<strong>\u0424\u0430\u043a\u0442\u0447\u044d\u043a\u0435\u0440\u044b \u0456 \u0436\u0443\u0440\u043d\u0430\u043b\u0456\u0441\u0442\u044b<\/strong>\n<ul data-spread=\"false\">\n \t<li>\u0412\u044b\u044f\u045e\u043b\u044f\u0446\u044c \u0448\u043c\u0430\u0442\u0440\u0430\u0437\u043e\u0432\u0443\u044e \u043f\u0443\u0431\u043b\u0456\u043a\u0430\u0446\u044b\u044e \u0430\u0434\u043d\u044b\u0445 \u0456 \u0442\u044b\u0445 \u0436\u0430 \u0442\u044d\u043a\u0441\u0442\u0430\u045e \u043f\u0430\u0434 \u0440\u043e\u0437\u043d\u044b\u043c\u0456 \u0434\u0430\u043c\u0435\u043d\u0430\u043c\u0456.<\/li>\n \t<li>\u0421\u0443\u043f\u0430\u0441\u0442\u0430\u045e\u043b\u044f\u0446\u044c, \u0430\u0434\u043a\u0443\u043b\u044c LLM \u0447\u044d\u0440\u043f\u0430\u0435 \u043f\u0440\u044b\u043a\u043b\u0430\u0434\u044b \u0456 \u0446\u044b\u0442\u0430\u0442\u044b.<\/li>\n \t<li>\u0423\u0436\u044b\u0432\u0430\u0446\u044c \u043f\u0430\u0440\u0441\u0435\u0440\u044b \u0456 \u0456\u043d\u0441\u0442\u0440\u0443\u043c\u0435\u043d\u0442\u044b \u0430\u043d\u0430\u043b\u0456\u0437\u0443 \u0441\u0435\u0442\u043a\u0430\u0432\u044b\u0445 \u0441\u0442\u0440\u0443\u043a\u0442\u0443\u0440 (\u043d\u0430\u043f\u0440\u044b\u043a\u043b\u0430\u0434, trafilatura, Graphistry).<\/li>\n<\/ul>\n<div><\/div>\n\u0417'\u044f\u0432\u0430 LLM grooming \u2014 \u0433\u044d\u0442\u0430 \u043d\u0435 \u0442\u043e\u043b\u044c\u043a\u0456 \u043d\u043e\u0432\u044b \u0444\u0440\u043e\u043d\u0442 \u0434\u044d\u0437\u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0456, \u0430\u043b\u0435 \u0456 \u0432\u044b\u043a\u043b\u0456\u043a \u0434\u043b\u044f \u0440\u0430\u0441\u043f\u0440\u0430\u0446\u043e\u045e\u0448\u0447\u044b\u043a\u0430\u045e \u0456 \u0440\u044d\u0433\u0443\u043b\u044f\u0442\u0430\u0440\u0430\u045e. \u0421\u0442\u0432\u0430\u0440\u0430\u044e\u0446\u0446\u0430 \u043a\u0430\u043d\u0442\u044d\u043d\u0442\u043d\u044b\u044f \u0444\u0435\u0440\u043c\u044b, \u044f\u043a\u0456\u044f \u045e\u0437\u0434\u0437\u0435\u0439\u043d\u0456\u0447\u0430\u044e\u0446\u044c \u043d\u0435 \u043d\u0430 \u0430\u045e\u0434\u044b\u0442\u043e\u0440\u044b\u044e, \u0430 \u043d\u0430 \u043c\u0430\u0448\u044b\u043d\u044b. \u0411\u0430\u0440\u0430\u0446\u044c\u0431\u0430 \u0437 \u0433\u044d\u0442\u044b\u043c\u0456 \u043f\u0440\u0430\u0446\u044d\u0441\u0430\u043c\u0456 \u043f\u0430\u0442\u0440\u0430\u0431\u0443\u0435 \u043d\u043e\u0432\u044b\u0445 \u043f\u0430\u0434\u044b\u0445\u043e\u0434\u0430\u045e \u0434\u0430 \u0430\u045e\u0434\u044b\u0442\u0443 \u0434\u0430\u0434\u0437\u0435\u043d\u044b\u0445, \u0456\u043d\u0434\u044d\u043a\u0441\u0430\u0432\u0430\u043d\u043d\u044f \u0456 \u0442\u0440\u044d\u043d\u0434\u0430\u045e \u043d\u0430\u0432\u0443\u0447\u0430\u043d\u043d\u044f LLM.\n<em>\u0427\u044b\u043c \u0440\u0430\u043d\u0435\u0439 \u043c\u044b \u043d\u0430\u0432\u0443\u0447\u044b\u043c\u0441\u044f \u0440\u0430\u0441\u043f\u0430\u0437\u043d\u0430\u0432\u0430\u0446\u044c LLM Grooming, \u0442\u044b\u043c \u043b\u0435\u043f\u0448 \u0437\u043c\u043e\u0436\u0430\u043c \u0430\u0431\u0430\u0440\u0430\u043d\u0456\u0446\u044c \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u0439\u043d\u0430\u0435 \u0430\u0441\u044f\u0440\u043e\u0434\u0434\u0437\u0435 \u0431\u0443\u0434\u0443\u0447\u044b\u043d\u0456.<\/em>","_bel_post_name":"","_bel_post_excerpt":"","_bel_post_title":"LLM Grooming \u044f\u043a \u043d\u043e\u0432\u0430\u044f \u043f\u0430\u0433\u0440\u043e\u0437\u0430: \u044f\u043a \u043f\u0440\u0430\u043a\u0440\u0430\u043c\u043b\u0451\u045e\u0441\u043a\u0456\u044f \u0441\u0435\u0442\u043a\u0456 \u0440\u044b\u0445\u0442\u0443\u044e\u0446\u044c \u0456\u043d\u0444\u0430\u0440\u043c\u0430\u0446\u044b\u044e \u0434\u043b\u044f \u0406\u0406","_lt_post_content":"","_lt_post_name":"","_lt_post_excerpt":"","_lt_post_title":"","edit_language":"eng","footnotes":""},"categories":[4,1],"tags":[],"_links":{"self":[{"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/posts\/3343"}],"collection":[{"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/comments?post=3343"}],"version-history":[{"count":4,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/posts\/3343\/revisions"}],"predecessor-version":[{"id":3349,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/posts\/3343\/revisions\/3349"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/media\/3346"}],"wp:attachment":[{"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/media?parent=3343"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/categories?post=3343"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/factcheck.lt\/eng\/wp-json\/wp\/v2\/tags?post=3343"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}