LLMs show "Authority Bias", accepting wrong answers from verified sources

By PulseAugur Editorial · [1 sources] · 2026-10-01 14:45

A new study reveals that large language models exhibit an AI

IMPACT This bias could make LLMs more susceptible to misinformation from external sources, impacting their reliability in agentic systems.

RANK_REASON Research paper detailing a specific LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

How we ranked this

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; Research paper detailing a specific LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, safety; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 2 days old; Coverage has settled into its steady-state source set.

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r/MachineLearning TIER_1 English(EN) · /u/MajorRedditor23 · 2026-10-01 14:45

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

<div class="md"><p>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 …