It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty 事件

It's Not Always Sycophancy: Measuring LLM Conformity as a Function of Epistemic Uncertainty arXiv:2605.27288v1 Announce Type: new Abstract: 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