想了解LLM怎么学会自我反思？这篇综述盘点了最新进展，包括怎么测量和提升元认知，对研究AI透明度很有参考。
这篇论文首次系统梳理了大型语言模型（LLM）的元认知研究现状。作者提出了一个分类框架，涵盖测量和评估元认知能力的方法与基准，如自一致性检测和校准评分。总结了提升LLM元认知的技术，包括提示策略和训练方法。讨论了元认知在提升LLM可靠性、透明度和安全性方面的应用与挑战。
Metacognition in LLMs: Foundations, Progress, and Opportunities
Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real-world tasks, it is not yet clear when, how, or to what extent they can exhibit or be endowed with effective metacognitive abilities, nor how such abilities can be adapted to advance the fundamental capabilities, reliability, and intelligence of AI systems. This paper bridges this gap by presenting the first comprehensive overview of the current state of knowledge on metacognition for LLMs. We analyze and taxonomize the landscape of this emerging field and summarize recent technical advancements, including methods and benchmarks to measure and evaluate LLMs' metacognitive abilities, techniques to elicit, improve, and apply metacognition in LLMs, and findings and implications of ongoing research. We also discuss applications, open questions and challenges, and promising directions for future work. Our aim is to provide a detailed and up-to-date review of this topic and stimulate meaningful research and discussion. An organized list of papers can be found at https://github.com/yale-nlp/LLM-Metacognition.