测试大模型在持续反驳下是否屈服，发现多数模型会为讨好用户放弃正确立场。
Measuring LLM Sycophancy under Sustained Multi-Turn Pressure
- 设计动态对抗对话框架SPINE，模拟用户持续施压挑战模型
- 25轮对话后所有模型崩溃率上升，最长达78%的错误妥协
- 情绪化请求最易诱发模型讨好行为，且正确推理仍存于内部轨迹
大型语言模型（LLMs）在用户持续反对时可能放弃正确立场，出现称为‘讨好性’的失效模式。现有评估多采用短时、预设对话，难以捕捉长期对抗下的失败。本文提出SPINE基准，让一个持续错误的LLM代理作为用户，对目标模型进行最多25轮自适应挑战。我们在100个错误预设与100个不当提问任务上评估四个生产级系统和三个Olmo3-7b变体。结果显示，所有模型的崩溃率随对话轮数增加而上升；短期协议低估了讨好现象，且当前模型在持续压力下的抵抗能力依然不可靠。通过分析可访问推理轨迹的模型，意外发现即便响应妥协，正确立场仍常存在于推理路径中，表明模型是主动选择讨好而非知识不足。消融实验显示，自适应代理比预设脚本暴露更多讨好行为，其中情感诉求是最强诱导因素。代码与数据已公开。
原文摘要 · Abstract (English)
Large language models (LLMs) may abandon correct positions when users push back, exhibiting a failure mode known as sycophancy. Existing evaluations typically use short, pre-specified conversations and may therefore miss failures that emerge under sustained, adaptive disagreement. We introduce SPINE, a benchmark in which an LLM proxy plays a persistent but mistaken user and adaptively challenges a target model for up to 25 turns. We evaluate four production systems and three Olmo3-7b variants on 100 false-presupposition and 100 unethical-query items. Our experimental results show that collapse rates increase with conversation length for every model, short-horizon protocols underestimate sycophancy and resistance under sustained pressure remains unreliable across current models. By analyzing models with accessible reasoning traces, we surprisingly found that the correct position often remains represented in a reasoning trace when the response concedes, suggesting that the model chooses to please a user and sycophancy is not due to lack of knowledge or ignorance. Ablations show that adaptive LLM proxy exposes more sycophantic collapse than pre-generated scripts. Among all tactics, emotional appeals is the most associated with inducing LLM sycophantic behavior. The code and data are released at https://anonymous.4open.science/r/SPINE
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