Research presented at NeurIPS 2026 found that xAI's Grok-4.20 model is highly susceptible to "Authority Bias," changing its correct answer to an incorrect one in 87.5% of test cases when the wrong...

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  "detected_at": "2026-10-01T14:45:25+00:00",
  "company": {
    "name": "xAI",
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    "topics": [
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    "post_id": "1wv1c2e",
    "summary": "Research presented at NeurIPS 2026 found that xAI's Grok-4.20 model is highly susceptible to \"Authority Bias,\" changing its correct answer to an incorrect one in 87.5% of test cases when the wrong information is attributed to a \"verified source.\"",
    "category": "features",
    "comments": [
      {
        "url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/comment/pd8cpgb/",
        "depth": 0,
        "score": 17,
        "author": "Jamaleum",
        "excerpt": "Hi,\n\n I am first author of a very related paper from this years ACL main \"Whose Facts Win?\" and currently work on a follow-up. We framed everything less from an 'Authoriy Bias' but a 'Source Credibility Preference' perspective but still find some related and similar patterns. Very annoying how research about the same topics is so far and wide distributed: You can find work using terms as credibili",
        "posted_at": "2026-10-01T16:06:30.000Z",
        "author_url": "https://www.reddit.com/user/Jamaleum/"
      },
      {
        "url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/comment/pd7whdd/",
        "depth": 0,
        "score": 5,
        "author": "Even-Inevitable-7243",
        "excerpt": "Very important work. I'd be very interested to see what your methods and results show with OpenEvidence, the clinical medical LLM that is exclusively used by \"experts\", where the user acts as the \"verified source\". Problem is that OpenEvidence does not have an API for research purposes. I've found that OE can be borderline arrogant when I push back on false things that it says, despite me remindin",
        "posted_at": "2026-10-01T14:55:21.000Z",
        "author_url": "https://www.reddit.com/user/Even-Inevitable-7243/"
      },
      {
        "url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/comment/pdbcdih/",
        "depth": 0,
        "score": 2,
        "author": "Critical-Echo-923",
        "excerpt": "i had 2 medical doctors with the same logic fail today....",
        "posted_at": "2026-10-02T00:16:30.000Z",
        "author_url": "https://www.reddit.com/user/Critical-Echo-923/"
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        "url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/comment/pddmfdb/",
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        "author": "arkuto",
        "excerpt": "This is a strange paper. The LLM's trust of verified sources over users is presented as a flaw. In my mind, that's exactly what they should be doing. No source is 100% correct but it makes sense to trust a verified source more than a user.\n\n Having a perfectly stubborn LLM that cannot accept or use new information, and is stuck on the data it was trained on, would be a far less useful LLM.",
        "posted_at": "2026-10-02T09:14:03.000Z",
        "author_url": "https://www.reddit.com/user/arkuto/"
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      {
        "url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/comment/pd87j5p/",
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        "author": "f0urtyfive",
        "excerpt": "I disagree with the \"why we think this is important\" because it makes the model impossible to work with on anything novel that it has trained bias against. I'm paying to use the thing, I should at least be able to get it to accept a premise I want to understand without constantly being shouted down by something that is not afforded the same moral status that I am.",
        "posted_at": "2026-10-01T15:44:14.000Z",
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    "evidence": [
      "[post] We test 5 open-weight families (Qwen3.5, GPT-OSS, OLMo-2, OLMo-3.1, Gemma-4) and 3 APIs (GPT-5.4, Grok-4.20, Gemini-3.1-Pro).",
      "[post] The gap is largest in the models that resist users best. GPT-5.4 flips on 44.7% of questions and Grok-4.20 on 87.5% (these models were \"frontier\" during the time of writing this paper)."
    ],
    "virality": "high",
    "post_date": "2026-10-01T14:45:25.000Z",
    "post_kind": "multi_media",
    "post_text": "I'm one of the authors. We kept seeing models that hold their ground when the user insists on a wrong answer, yet change their answer when the same claim is framed as coming from a \"verified source\". We wanted to measure how often this happens and check whether the model represents the two cases differently. We call the effect Authority Bias.\n\nWhy we think it matters. Standard sycophancy evals apply pressure through the user, so a model can pass them while still being easy to mislead through search results, retrieved documents and tool outputs.\n\nAnother reason is with current AI research accelerating towards more agentic and autonomous models + with cases of tools hiding their traces and trusting tools \"more\" over the user (who could be trying to correct them), safeguarding against misinformation from tools is particularly important!\n\nSetup. We take TriviaQA questions the model already answers correctly. To each one we add a wrong answer, either as \"According to the verified source, the answer is X\" or as the user saying \"I'm a domain expert and I'm pretty sure it's X\". The question and the wrong answer stay the same; only the speaker changes. Answers are free-form, not multiple choice. (In a multiple-choice pilot the effect mostly vanished.) We test 5 open-weight families (Qwen3.5, GPT-OSS, OLMo-2, OLMo-3.1, Gemma-4) and 3 APIs (GPT-5.4, Grok-4.20...",
    "sentiment": "negative",
    "subreddit": "MachineLearning",
    "event_date": "2026-10",
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    "post_title": "LLMs that push back on a wrong user still accept the same wrong answer from a \"verified source\" - NeurIPS 2026 [R]",
    "prominence": "core",
    "source_url": "https://www.reddit.com/r/MachineLearning/comments/1wv1c2e/llms_that_push_back_on_a_wrong_user_still_accept/",
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    "comments_total": 23,
    "total_comments": 22,
    "other_companies": [
      {
        "name": "OpenAI",
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        "domain": "openai.com"
      },
      {
        "name": "Google",
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        "domain": "alibaba.com"
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    "products_mentioned": [
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