比较人类与大模型的元认知能力，探索如何提升AI的自我判断力。
Metacognition and Uncertainty Communication in Humans and Large Language Models
- 通过对比人类与大模型在知识监控和评估上的表现，分析其异同。
- 发现两者在部分行为上相似，但本质差异显著影响人机协作。
- 未来增强模型的元认知可推动自主学习与好奇心发展。
元认知——对自身知识和表现进行监控与评估的能力——是人类决策、学习和沟通的基础。随着大语言模型（LLMs）越来越多地应用于高风险与日常低风险场景，评估它们是否具备元认知能力、以何种方式以及程度如何变得至关重要。本文综述了当前关于大模型元认知能力的研究进展，探讨其研究方法，并将其与人类元认知知识相联系。结果显示，尽管人类与大模型在某些元认知行为上表现出相似性，但诸多根本差异依然存在；关注这些差异对于改善人机协作具有重要意义。最后，我们讨论了赋予未来大模型更敏感、更校准的元认知能力，可能帮助其发展出更高效的学习、自我导向和好奇心等新能力。
原文摘要 · Abstract (English)
Metacognition--the capacity to monitor and evaluate one's own knowledge and performance--is foundational to human decision-making, learning, and communication. As large language models (LLMs) become increasingly embedded in both high-stakes and widespread low-stakes contexts, it is important to assess whether, how, and to what extent they exhibit metacognitive abilities. Here, we provide an overview of current knowledge of LLMs' metacognitive capacities, how they might be studied, and how they relate to our knowledge of metacognition in humans. We show that while humans and LLMs can sometimes appear quite aligned in their metacognitive capacities and behaviors, it is clear many differences remain; attending to these differences is important for enhancing human-AI collaboration. Finally, we discuss how endowing future LLMs with more sensitive and more calibrated metacognition may also help them develop new capacities such as more efficient learning, self-direction, and curiosity.
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