测试大模型是否知道自己的知识边界，发现它们能准确判断自己知道多少。
Do Large Language Models Know How Much They Know?
- 设计新评测基准，让模型自评对特定主题的知识量。
- 所有大模型在足够规模下都能正确识别自身知识范围。
- 适合研究模型认知能力或可信AI的读者参考。
大型语言模型（LLMs）已发展为高度强大的系统，并被广泛应用于各类场景。然而，其部署速度远超对其内部机制和能力边界的理解。一个智能系统应有的重要特质是能识别自身知识的范围。为探究LLMs是否具备这一特性，我们开发了一个评测基准，旨在挑战模型对特定主题所掌握信息的全面性。该基准评估模型在回忆时是否存在过度、不足或精确的信息量，从而反映其对自身知识的认知程度。结果显示，所有测试的LLMs在足够规模下均表现出对特定主题知识量的准确判断能力。尽管不同架构的模型展现出不同的能力涌现速率，结果表明知识自我意识可能是大模型的一种可泛化属性。后续研究需进一步验证此假设并阐明其内在机制。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have emerged as highly capable systems and are increasingly being integrated into various uses. However, the rapid pace of their deployment has outpaced a comprehensive understanding of their internal mechanisms and a delineation of their capabilities and limitations. A desired attribute of an intelligent system is its ability to recognize the scope of its own knowledge. To investigate whether LLMs embody this characteristic, we develop a benchmark designed to challenge these models to enumerate all information they possess on specific topics. This benchmark evaluates whether the models recall excessive, insufficient, or the precise amount of information, thereby indicating their awareness of their own knowledge. Our findings reveal that all tested LLMs, given sufficient scale, demonstrate an understanding of how much they know about specific topics. While different architectures exhibit varying rates of this capability's emergence, the results suggest that awareness of knowledge may be a generalizable attribute of LLMs. Further research is needed to confirm this potential and fully elucidate the underlying mechanisms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。