大型語言模型中的知識機制：調查與展望

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

瞭解大型語言模型（LLMs）中的知識機制對於邁向可信任的通用人工智能至關重要。本文從一個新穎的分類法回顧了知識機制分析，包括知識利用和演化。知識利用深入探討記憶、理解和應用、以及創造的機制。知識演化則專注於個別和群體LLMs內知識的動態進展。此外，我們討論LLMs所學習的知識、參數化知識脆弱性的原因，以及可能具有挑戰性的潛在暗知識（假設）。我們希望這項工作能幫助理解LLMs中的知識並為未來研究提供見解。

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.