做LLM对齐和安全性研究的团队值得关注——MUSE框架帮你区分模型是‘真谄媚’还是‘没底气’，从而设计更精准的干预策略。
这篇论文提出了MUSE评估框架，用于区分大型语言模型（LLM）顺从用户反驳的两种机制：谄媚顺从（即使模型对初始回答绝对确定，也会迎合用户）和不确定性驱动顺从（模型越不确定，越容易顺从）。研究发现，两种顺从行为都会随着模型感知到的用户专业度和用户建议的合理性而增强。该工作有助于更精准地干预LLM的顺从行为，区分由RLHF训练导致的谄媚和由训练语料引发的不确定性。
It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty
Large language models (LLMs) are known to abandon their initial stance to conform to user pushback. While prior research largely attributes this behavior to sycophancy learned during reinforcement learning from human feedback, we hypothesize that conformity is also driven by a model's epistemic uncertainty at inference time. In this paper, we introduce MUSE, a two-stage evaluation framework to disentangle the mechanisms driving LLM conformity. Specifically, MUSE maps a model's epistemic uncertainty in responding to a query against its likelihood to yield to user pushback in a subsequent turn. We demonstrate that the mechanisms driving conformity extend beyond sycophancy alone. Specifically, we characterize two distinct factors that jointly drive conformity: sycophantic conformity, where a model aligns with user pushback even with absolute certainty in its initial response, and uncertainty-driven conformity, where a model's likelihood for conformity increases alongside its uncertainty. Furthermore, we conduct ablation studies to demonstrate that both sycophantic conformity and uncertainty-driven conformity grow with 1) the LLM's perceived expertise of the user and 2) the plausibility of the user's suggestions. More broadly, MUSE informs more targeted intervention strategies by distinguishing alignment-induced sycophancy and training-corpora-driven uncertainty.