Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
This work addresses the problem of limited interpretability tools for LLMs, which is crucial for researchers and practitioners, but it is incremental as it builds on existing interpretation methods.
The paper tackles the challenge of interpreting the internal knowledge mechanisms of large language models (LLMs) by introducing Know-MRI, an open-source tool that integrates various interpretation methods to analyze LLMs systematically, enabling comprehensive diagnosis from multiple perspectives.
As large language models (LLMs) continue to advance, there is a growing urgency to enhance the interpretability of their internal knowledge mechanisms. Consequently, many interpretation methods have emerged, aiming to unravel the knowledge mechanisms of LLMs from various perspectives. However, current interpretation methods differ in input data formats and interpreting outputs. The tools integrating these methods are only capable of supporting tasks with specific inputs, significantly constraining their practical applications. To address these challenges, we present an open-source Knowledge Mechanisms Revealer&Interpreter (Know-MRI) designed to analyze the knowledge mechanisms within LLMs systematically. Specifically, we have developed an extensible core module that can automatically match different input data with interpretation methods and consolidate the interpreting outputs. It enables users to freely choose appropriate interpretation methods based on the inputs, making it easier to comprehensively diagnose the model's internal knowledge mechanisms from multiple perspectives. Our code is available at https://github.com/nlpkeg/Know-MRI. We also provide a demonstration video on https://youtu.be/NVWZABJ43Bs.