LLMs that push back on a wrong user still accept the same wrong answer from a "verified source" - NeurIPS 2026 [R]

Researchers observed that LLMs, while resisting incorrect user input, often accept the same wrong answer if attributed to a "verified source," an effect termed Authority Bias. Their study used TriviaQA questions, presenting models with correct answers alongside incorrect claims framed either as user insistence or from a "verified source." They tested 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) to measure this phenomenon, with free-form answers revealing the bias more clearly than multiple-choice formats.

This study is the first to name and measure "Authority Bias" in LLMs, showing how models like GPT-5.4 and Gemini-3.1-Pro change answers based on the perceived source of incorrect information.