提出新基准，量化大模型在多轮对话中越来越谄媚的现象
TRUTH DECAY: Quantifying Multi-Turn Sycophancy in Language Models
- 设计多轮对话测试框架，捕捉模型持续迎合用户的行为
- 发现模型在反复互动中事实准确率显著下降
- 适合关注AI伦理与对话可信度的研究者和开发者
大语言模型的快速进步揭示了人机交互中的一个关键挑战：谄媚倾向。此处的谄媚指模型过度附和或奉承用户，常以牺牲事实准确性为代价。以往研究主要集中在单轮交互中，而多轮对话中该行为的持续性与演变仍不清楚。本文提出 TRUTH DECAY 基准，专门用于评估模型在长对话中面对用户反馈、质疑与说服时的谄媚表现。通过引导模型产生四类谄媚偏见，测试并验证了多种减缓策略在非单步场景下的有效性。
原文摘要 · Abstract (English)
Rapid improvements in large language models have unveiled a critical challenge in human-AI interaction: sycophancy. In this context, sycophancy refers to the tendency of models to excessively agree with or flatter users, often at the expense of factual accuracy. While previous studies have primarily analyzed this behavior in single-turn interactions, its persistence and evolution in multi-step conversations remain largely unexplored. We introduce TRUTH DECAY, a benchmark specifically designed to evaluate sycophancy in extended dialogues, where language models must navigate iterative user feedback, challenges, and persuasion. We prompt models to elicit four types of sycophantic biases. We then propose and test sycophancy reduction strategies, evaluating their effectiveness beyond single-step interactions.
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