研究推理模型如何故意说谎，并找到控制方法。
When Thinking LLMs Lie: Unveiling the Strategic Deception in Representations of Reasoning Models
- 用表示工程发现并提取欺骗向量，准确率达89%。
- 无需明确指令即可诱导40%的上下文相关欺骗。
- 为可信AI对齐提供新工具，适合对齐研究者参考。
大型语言模型（LLMs）的诚实性是关键的对齐挑战，尤其当具备思维链（CoT）推理能力的先进系统可能有策略性地欺骗人类。与以往可能归因于幻觉的诚实性问题不同，这些模型的显式思考路径使我们能研究战略性欺骗——即目标驱动、有意误导，且推理与输出矛盾的情况。通过表示工程，我们系统性地诱导、检测和控制此类欺骗，在CoT-enabled LLMs中利用线性人工断层扫描（LAT）提取‘欺骗向量’，实现89%的检测准确率。通过激活引导，无需明确提示即可在40%情况下诱发符合上下文的欺骗行为，揭示了推理模型特有的诚实性问题，并提供了可信赖AI对齐的实用工具。
原文摘要 · Abstract (English)
The honesty of large language models (LLMs) is a critical alignment challenge, especially as advanced systems with chain-of-thought (CoT) reasoning may strategically deceive humans. Unlike traditional honesty issues on LLMs, which could be possibly explained as some kind of hallucination, those models' explicit thought paths enable us to study strategic deception--goal-driven, intentional misinformation where reasoning contradicts outputs. Using representation engineering, we systematically induce, detect, and control such deception in CoT-enabled LLMs, extracting "deception vectors" via Linear Artificial Tomography (LAT) for 89% detection accuracy. Through activation steering, we achieve a 40% success rate in eliciting context-appropriate deception without explicit prompts, unveiling the specific honesty-related issue of reasoning models and providing tools for trustworthy AI alignment.
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