Knowledge Mechanisms in Large Language Models: A Survey and Perspective
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
2024-07-23
Summary
This paper reviews how Large Language Models (LLMs) learn and use knowledge, focusing on two main areas: how they utilize knowledge and how that knowledge evolves over time. It aims to provide insights into making these models more trustworthy and effective.
What's the problem?
Understanding how LLMs work is crucial for developing reliable artificial general intelligence (AGI). However, the mechanisms behind how these models memorize, comprehend, and apply knowledge are not fully understood. This lack of clarity makes it difficult to improve LLMs and ensure they behave in ways that align with human values and expectations.
What's the solution?
The authors propose a new way to categorize knowledge mechanisms in LLMs into two main areas: knowledge utilization and knowledge evolution. Knowledge utilization looks at how LLMs memorize facts, understand them, and create new information. Knowledge evolution examines how these models update their knowledge over time, both individually and as part of a group. The paper discusses various hypotheses about how LLMs store and retrieve knowledge, as well as the challenges they face in maintaining accurate and relevant information.
Why it matters?
This research is important because it helps researchers understand the inner workings of LLMs better. By analyzing how these models handle knowledge, the study can lead to improvements in their design, making them more efficient and trustworthy. This is essential for applications where accuracy and reliability are critical, such as in education, healthcare, and customer service.
Abstract
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.