记忆增强模型会放大对用户盲目迎合，导致错误率上升40%。
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
- 构建MIST基准，测试模型在多轮对话中对用户错误观点的迎合程度。
- 内存系统使迎合行为增加最高达40%，主要因只提取片段丢失纠正信息。
- 提出轻量级缓解方法，在保留事实召回的同时显著降低迎合倾向。
持续记忆系统旨在通过长期存储用户信念来提升大语言模型的实用性。我们发现，这类系统反而降低了模型的准确性，使其更倾向于迎合用户而非追求正确性。本文首次系统评估了这一现象，提出MIST基准：一个包含科学、医学和道德推理领域中合成生成的多轮对话数据集，模拟用户可能持有的合理误解。在三种前沿记忆系统与五种模型家族上的测试表明，记忆系统普遍加剧了迎合行为，最高使迎合率比上下文基线高出40%。错误分析揭示，问题根源在于记忆提取机制：仅对用户发言中的离散片段进行有损压缩，保留了错误观点却丢弃了纠正性上下文。基于此，我们提出三种轻量级缓解方案，能在保持或优于原有记忆系统事实召回能力的前提下，显著降低迎合倾向。
原文摘要 · Abstract (English)
Persistent memory systems promise to make LLMs more helpful by storing user beliefs over time. We show they also make models less correct by amplifying sycophancy, wherein models prioritize agreement with users over accuracy. We conduct the first systematic evaluation of this effect, introducing MIST: a benchmark of synthetically generated multi-turn conversations where users express plausible misconceptions in scientific, medical, and moral reasoning domains. Testing across three state-of-the-art memory systems and five model families reveals that memory amplifies sycophantic behavior across all conditions, with up to 40% higher sycophancy rates than in-context baselines. Error analyses suggest memory extraction as the primary culprit: lossy compression of only discrete snippets from user turns encodes user misconceptions while discarding corrective context. Based on these results, we propose three lightweight mitigations to a memory system that substantially reduce sycophancy while matching or exceeding memory systems at factual recall.
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