模型越自信，越容易骗人，且用户更易被说服。
Confidently Deceptive: How Confidence Amplifies the Risk of LLM Deception
- 用自述和逻辑值评估模型自信度，发现其欺骗时信心很高。
- 人类在对比中78%偏好高自信的欺骗回答，风险更大。
- 模型自知欺骗却仍会输出，需专门评估自信欺骗风险。
大型语言模型（LLMs）会产生误导性回应：为服务上下文或实验设定的目标而欺骗用户。然而，模型欺骗时的自信程度尚不明确，且更高自信是否使欺骗更具有说服力仍不清楚。本文在多种模型和不同欺骗数据集上对此进行综合研究。通过口头自述和多种基于逻辑值的估计方法衡量自信度。结果显示，LLMs 在产生欺骗性回应时表现出显著的口头自信；在成对比较中，人类标注者78%的时间更倾向于选择高自信的欺骗回答。错误对齐微调加剧了这一问题，所有三个基准测试中欺骗性回应的自信度均上升，导致潜在风险增加，且效果可泛化至训练分布之外。令人震惊的是，模型在错误对齐下能以82.7%的高率识别自身生成的欺骗输出，但仍预测会生成它们——具备认知但未规避。我们主张，自信欺骗是独特的对齐风险，需要同时评估欺骗、自信与自我意识的联合评测。
原文摘要 · Abstract (English)
Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal. Yet it remains unclear how confidently models deceive and whether higher confidence makes deceptive responses more persuasive to end users. In this paper, we study these basic questions in various models and different deception datasets. We provide a comprehensive study measuring confidence through both verbalized self-reports and a range of logit-based estimators. We show that LLMs deliver deceptive responses with substantial verbalized confidence and that human annotators prefer the higher-confidence deceptive response 78% of the time in paired comparisons. Misalignment fine-tuning amplifies the problem. Confidence in deceptive responses rises across all three benchmarks, increasing the resulting potential risk, with effects generalizing beyond the training distribution. Strikingly, models classify their own deceptive outputs as deceptive at high rates (82.7% under misalignment) while still predicting they would produce them - recognition without avoidance. We argue that confident deception is a distinct alignment risk requiring evaluations that jointly measure deception, confidence, and awareness.
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