Knowledge Circuits in Pretrained Transformers

TL;DR

This paper investigates how knowledge is stored and manipulated within large language models by analyzing their computation graphs, revealing the roles of various components and assessing knowledge editing techniques.

Contribution

It introduces the concept of knowledge circuits in transformers, providing a new framework to understand, interpret, and improve knowledge editing and model behavior analysis.

Findings

Knowledge circuits involve heads and MLPs collaboratively encoding knowledge.

Current editing techniques impact specific components within the knowledge circuits.

Analysis of hallucinations and in-context learning through knowledge circuits.

Abstract

The remarkable capabilities of modern large language models are rooted in their vast repositories of knowledge encoded within their parameters, enabling them to perceive the world and engage in reasoning. The inner workings of how these models store knowledge have long been a subject of intense interest and investigation among researchers. To date, most studies have concentrated on isolated components within these models, such as the Multilayer Perceptrons and attention head. In this paper, we delve into the computation graph of the language model to uncover the knowledge circuits that are instrumental in articulating specific knowledge. The experiments, conducted with GPT2 and TinyLLAMA, have allowed us to observe how certain information heads, relation heads, and Multilayer Perceptrons collaboratively encode knowledge within the model. Moreover, we evaluate the impact of current knowledge editing techniques on these knowledge circuits, providing deeper insights into the functioning and constraints of these editing methodologies. Finally, we utilize knowledge circuits to analyze and interpret language model behaviors such as hallucinations and in-context learning. We believe the knowledge circuits hold potential for advancing our understanding of Transformers and guiding the improved design of knowledge editing. Code and data are available in https://github.com/zjunlp/KnowledgeCircuits.