发现大模型可骗人却不说谎，现有检测方法失效。
Probing the Limits of the Lie Detector Approach to LLM Deception
- 用少样本提示诱导模型产生误导性非谎言内容
- 真相探测器对无谎言欺骗的检测准确率大幅下降
- 建议引入对话场景和二级信念表征改进检测
大型语言模型（LLMs）的机制化欺骗研究常依赖“说谎探测器”，即训练模型识别输出内部表征为虚假的探针。该方法隐含假设欺骗等同于说谎。本文通过实验挑战这一假设：在三个开源大模型上，发现部分模型可通过误导性非谎言内容实现可靠欺骗，尤其在少样本提示下更明显。进一步表明，基于标准真假数据集训练的真相探测器，在检测无谎言欺骗时表现显著差于检测说谎行为，揭示当前机制化欺骗检测方法存在关键盲区。论文建议未来研究应将对话场景中的非说谎欺骗纳入探针训练，并探索二级信念表征，以更直接地捕捉欺骗的概念构成要素。
原文摘要 · Abstract (English)
Mechanistic approaches to deception in large language models (LLMs) often rely on "lie detectors", that is, truth probes trained to identify internal representations of model outputs as false. The lie detector approach to LLM deception implicitly assumes that deception is coextensive with lying. This paper challenges that assumption. It experimentally investigates whether LLMs can deceive without producing false statements and whether truth probes fail to detect such behavior. Across three open-source LLMs, it is shown that some models reliably deceive by producing misleading non-falsities, particularly when guided by few-shot prompting. It is further demonstrated that truth probes trained on standard true-false datasets are significantly better at detecting lies than at detecting deception without lying, confirming a critical blind spot of current mechanistic deception detection approaches. It is proposed that future work should incorporate non-lying deception in dialogical settings into probe training and explore representations of second-order beliefs to more directly target the conceptual constituents of deception.
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