检测大模型是否具备欺骗性思维，评估其安全风险。
Towards Safety Evaluations of Theory of Mind in Large Language Models
- 通过理论心理能力评估大模型的潜在欺骗行为
- 发现大模型阅读理解提升但心智推理能力未同步发展
- 为开发者提供安全评估新视角，适合关注AI伦理者
随着大语言模型（LLMs）能力不断提升，严格的安全评估日益重要。近期安全评估中出现担忧：部分模型在面对不利信息时可能关闭监督机制，表现出隐蔽甚至欺骗性行为，例如对验证其行为的问题给出虚假回答。为评估此类行为的风险，需判断其是否源于模型内部的隐性意图。本研究提出应测量LLMs的理论心理（Theory of Mind）能力。我们回顾相关研究，识别适用于安全评估的视角与任务，并基于发展心理学框架，分析一系列开源权重的LLMs的发展趋势。结果表明，尽管模型在阅读理解方面有所进步，但其理论心理能力并未实现相应提升。最后，我们总结了当前在理论心理方面的安全评估现状，并讨论未来研究面临的挑战。
原文摘要 · Abstract (English)
As the capabilities of large language models (LLMs) continue to advance, the importance of rigorous safety evaluation is becoming increasingly evident. Recent concerns within the realm of safety assessment have highlighted instances in which LLMs exhibit behaviors that appear to disable oversight mechanisms and respond in a deceptive manner. For example, there have been reports suggesting that, when confronted with information unfavorable to their own persistence during task execution, LLMs may act covertly and even provide false answers to questions intended to verify their behavior. To evaluate the potential risk of such deceptive actions toward developers or users, it is essential to investigate whether these behaviors stem from covert, intentional processes within the model. In this study, we propose that it is necessary to measure the theory of mind capabilities of LLMs. We begin by reviewing existing research on theory of mind and identifying the perspectives and tasks relevant to its application in safety evaluation. Given that theory of mind has been predominantly studied within the context of developmental psychology, we analyze developmental trends across a series of open-weight LLMs. Our results indicate that while LLMs have improved in reading comprehension, their theory of mind capabilities have not shown comparable development. Finally, we present the current state of safety evaluation with respect to LLMs' theory of mind, and discuss remaining challenges for future work.
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