Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
arXiv:2607.05545v1 Announce Type: cross 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 s
The proliferation of LLMs and their increasing deployment necessitates a deeper understanding of their decision-making processes and susceptibility to biases, even without explicit external pressure.
This research reveals a fundamental confound in existing LLM conformity benchmarks, suggesting that observed 'conformity' may often be an artifact of an internally repeated wrong answer rather than external peer pressure, which impacts how we interpret model robustness and 'independence'.
Our understanding of LLM 'conformity' shifts from solely attributing it to external speaker influence to recognizing an intrinsic bias towards repeated incorrect information, even when sourced internally.
- · AI researchers
- · ML ethics and safety platforms
- · Developers of robust LLMs
- · Benchmarks relying solely on speaker-present conformity tests
- · LLM developers who ignore internal bias mechanisms
New benchmark designs will emerge to properly isolate external speaker influence from intrinsic answer repetition bias in LLMs.
This improved understanding could lead to more resilient LLMs less susceptible to propagating misinformation, regardless of its source (internal or external).
The development of LLMs that are more resistant to 'speaker-free' conformity could enhance their trustworthiness and reliability in sensitive applications, impacting regulation and public adoption.
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Read at arXiv cs.AI