Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
A new study reveals that LLM conformity in benchmarks is largely due to repeated wrong answers rather than social influence. Introducing a no-source condition, researchers found that 66.5% of correct answers are changed to harmful ones even without a speaker, compared to 10.3% under plain re-ask. This suggests benchmarks should measure the speaker-free floor first.
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[Submitted on 6 Jul 2026]
Title:Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
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Abstract:LLM conformity is often used to describe cases where a model changes a correct answer toward a peer or group response. We show that most of this apparent conformity survives even after the peer is removed. The reason is a confound: standard conformity prompts mix two cues at once, the presence of a speaker and the repeated wrong answer itself. Existing benchmarks vary these cues together, so they cannot tell how much of the revision actually depends on the speaker. We introduce a no-source condition: the same asserted answer with the explicit speaker removed. Across six open-weight LLMs and seven QA and reasoning datasets, this condition alone causes harmful revision in $66.5\%$ of initially correct cases, compared with $10.3\%$ under a plain re-ask. The effect also remains when the repeated answer is paraphrased and when answer options are hidden in an open-ended setting. Source framing mainly modulates this floor: expert-panel framing raises it, while minimal person labels do not reliably raise it. When models flip, they are usually confidently wrong, and simple recalibration does not recover the original answer. Source attribution still matters, but it should be measured as an increment above this speaker-free floor. The methodological lesson is that conformity benchmarks should first measure what remains after the speaker is removed; without this step, benchmarks may mistake repeated text for social influence.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.05545 [cs.CL]
(or arXiv:2607.05545v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.05545
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yibo Hu [view email] [v1] Mon, 6 Jul 2026 18:27:19 UTC (130 KB)
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