@inproceedings{liu-etal-2025-know,
title = "Know-{MRI}: A Knowledge Mechanisms Revealer{\&}Interpreter for Large Language Models",
author = "Liu, Jiaxiang and
Xing, Boxuan and
Yuan, Chenhao and
ChenxiangZhang, ChenxiangZhang and
Wu, Di and
Huang, Xiusheng and
Yu, Haida and
Lang, Chuhan and
Cao, Pengfei and
Zhao, Jun and
Liu, Kang",
editor = "Mishra, Pushkar and
Muresan, Smaranda and
Yu, Tao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-demo.20/",
doi = "10.18653/v1/2025.acl-demo.20",
pages = "199--210",
ISBN = "979-8-89176-253-4",
abstract = "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 **Know**ledge **M**echanisms **R**evealer{\&}**I**nterpreter (**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."
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<abstract>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 **Know**ledge **M**echanisms **R**evealer&**I**nterpreter (**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.</abstract>
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%0 Conference Proceedings
%T Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
%A Liu, Jiaxiang
%A Xing, Boxuan
%A Yuan, Chenhao
%A ChenxiangZhang, ChenxiangZhang
%A Wu, Di
%A Huang, Xiusheng
%A Yu, Haida
%A Lang, Chuhan
%A Cao, Pengfei
%A Zhao, Jun
%A Liu, Kang
%Y Mishra, Pushkar
%Y Muresan, Smaranda
%Y Yu, Tao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-253-4
%F liu-etal-2025-know
%X 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 **Know**ledge **M**echanisms **R**evealer&**I**nterpreter (**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.
%R 10.18653/v1/2025.acl-demo.20
%U https://aclanthology.org/2025.acl-demo.20/
%U https://doi.org/10.18653/v1/2025.acl-demo.20
%P 199-210
Markdown (Informal)
[Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models](https://aclanthology.org/2025.acl-demo.20/) (Liu et al., ACL 2025)
ACL
- Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, and Kang Liu. 2025. Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 199–210, Vienna, Austria. Association for Computational Linguistics.