arXiv cs.AI· Jean Seo, Pepa Atanasova, Isabelle Augenstein·· 3 天前AI 评分38
用户主张的表述方式如何驱动 LLM 谄媚行为
Claim Framing as a Driver of LLM Sycophancy
AI 导读
一项针对 14 个模型、42K 道事实性问题的系统研究发现，所有 LLM 无论规模大小都易受用户观点影响，其中观点表述的自信程度、归属对象以及是否被呈现为用户与模型共有这三点影响最大。与以往研究不同，基于用户专业性或权威性的表述在事实性场景中影响甚微。更大模型更能抵抗错误观点，但从自身错误中恢复的能力并未更强。
正文 · 原文
Abstract:Sycophancy in Large Language Models, the tendency to align responses with user preferences over ground truth, has largely been studied using questions without a clear ground truth and with a single, plainly stated user opinion. We present a systematic study of which framings of a user's claim trigger sycophancy in factual question answering, where every answer is objectively verifiable without extra context. Evaluating 14 models on 42K questions, we express correct and incorrect user opinions through various claim framings over 8 dimensions and 21 types, and measure a broad set of opinion-driven behaviors. We find that all models are susceptible to user opinions regardless of size. Models are most influenced by how confidently an opinion is stated, whom it is attributed to, and whether it is presented as shared between the user and the model. In contrast to prior work, framings based on the user's expertise, authority over the model, or social pressure matter little in our factual setting. Larger models resist incorrect opinions better but are no better at recovering from their own errors. Our results call for sycophancy evaluations that probe how an opinion is framed, not merely whether one is present, and that measure multiple behaviors beyond a single flip rate.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.18159 [cs.CL] |
| (or arXiv:2610.18159v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18159 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jean Seo [view email]
[v1]
Tue, 13 Oct 2026 11:02:55 UTC (10,384 KB)
来源：arXiv cs.AI · arxiv.org