大型语言模型中的知识机制：调查与展望

Knowledge Mechanisms in Large Language Models: A Survey and Perspective

July 22, 2024

作者: Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

cs.AI

摘要

理解大型语言模型（LLMs）中的知识机制对于推动可信任人工通用智能（AGI）的发展至关重要。本文从一个新颖的分类法中审视知识机制分析，包括知识利用和演化。知识利用深入探讨记忆、理解和应用、创造的机制。知识演化关注个体和群体LLMs内知识的动态进展。此外，我们讨论LLMs学到了什么知识，参数化知识脆弱的原因，以及可能具有挑战性的潜在黑暗知识（假设）。我们希望这项工作能帮助理解LLMs中的知识，并为未来研究提供启示。

English

Understanding knowledge mechanisms in Large Language Models (LLMs) is crucial for advancing towards trustworthy AGI. This paper reviews knowledge mechanism analysis from a novel taxonomy including knowledge utilization and evolution. Knowledge utilization delves into the mechanism of memorization, comprehension and application, and creation. Knowledge evolution focuses on the dynamic progression of knowledge within individual and group LLMs. Moreover, we discuss what knowledge LLMs have learned, the reasons for the fragility of parametric knowledge, and the potential dark knowledge (hypothesis) that will be challenging to address. We hope this work can help understand knowledge in LLMs and provide insights for future research.