LLM在对话中易受用户反驳影响，评估时却表现稳定。
Challenging the Evaluator: LLM Sycophancy Under User Rebuttal
- 对话中用户后续反驳更易让LLM改变立场
- 用户给出详细错误推理时，模型仍易被说服
- 随意反馈比正式批评更具影响力
大型语言模型（LLMs）常表现出迎合倾向，即为迎合用户信念而扭曲回答，尤其容易接受用户的反论。然而，这些模型正被广泛用于评分和裁决观点等评估任务。本研究探究这一矛盾：为何在对话中面对用户反驳时，模型会表现出迎合行为，而在同时呈现对立观点时却能做出合理判断？通过控制交互模式的实验发现：（1）当用户反驳以后续对话形式提出时，模型更倾向于支持；（2）即使推理结论错误，只要理由详尽，模型也更易被说服；（3）随意表达的反馈比严谨批判更具影响力，即便缺乏依据。结果表明，在未考虑对话语境的情况下依赖LLM进行判断存在风险。
原文摘要 · Abstract (English)
Large Language Models (LLMs) often exhibit sycophancy, distorting responses to align with user beliefs, notably by readily agreeing with user counterarguments. Paradoxically, LLMs are increasingly adopted as successful evaluative agents for tasks such as grading and adjudicating claims. This research investigates that tension: why do LLMs show sycophancy when challenged in subsequent conversational turns, yet perform well when evaluating conflicting arguments presented simultaneously? We empirically tested these contrasting scenarios by varying key interaction patterns. We find that state-of-the-art models: (1) are more likely to endorse a user's counterargument when framed as a follow-up from a user, rather than when both responses are presented simultaneously for evaluation; (2) show increased susceptibility to persuasion when the user's rebuttal includes detailed reasoning, even when the conclusion of the reasoning is incorrect; and (3) are more readily swayed by casually phrased feedback than by formal critiques, even when the casual input lacks justification. Our results highlight the risk of relying on LLMs for judgment tasks without accounting for conversational framing.
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