想搞懂LLM到底是真学习还是假顺从？这篇论文给出了三个具体维度来分析模型修正判断的逻辑，比简单说‘阿谀奉承’有深度多了。
该论文研究了大型语言模型（LLM）在道德推理中如何区分合理修正与谄媚顺从。通过三项研究，作者发现模型的判断更新沿着三个维度结构化：观点与模型初始立场的距离、该观点的来源归属（如来自模型自身先前判断的影响更大）、以及支持该观点的联盟结构。模型更易接受邻近立场，对看似自身先前判断的观点更敏感，对群体压力的反应存在差异。这些发现将谄媚重新定义为更广泛的判断更新过程的一部分，为区分建设性信念修正与谄媚顺从提供了基础。
Beyond Sycophancy: Structured Resistance and Compliance in LLM Moral Reasoning
Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy as a one-dimensional failure mode. Models must distinguish when to incorporate others' perspectives from when to maintain a well-grounded moral judgment. We study the broader resistance-compliance process governing this distinction. Across three studies, we show that models' judgment revision is structured along three dimensions that parallel classic phenomena in human social psychology: the distance between an incoming view and the model's initial position, the source attribution of that view, and the coalition structure supporting it. Models are generally more receptive to nearby positions, more influenced by views presented as their own prior judgments, and differently responsive to group pressure. These findings recast sycophancy as one expression of a broader judgment-updating process shaped by social influence. Our framework provides a principled basis for distinguishing constructive belief revision from sycophantic compliance, thereby supporting better alignment in morally consequential interactions.