Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models
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arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910998504407040 |
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| author | Liu, Jiaxiang Xing, Boxuan Yuan, Chenhao Zhang, Chenxiang Wu, Di Huang, Xiusheng Yu, Haida Lang, Chuhan Cao, Pengfei Zhao, Jun Liu, Kang |
| author_facet | Liu, Jiaxiang Xing, Boxuan Yuan, Chenhao Zhang, Chenxiang Wu, Di Huang, Xiusheng Yu, Haida Lang, Chuhan Cao, Pengfei Zhao, Jun Liu, Kang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08427 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models Liu, Jiaxiang Xing, Boxuan Yuan, Chenhao Zhang, Chenxiang Wu, Di Huang, Xiusheng Yu, Haida Lang, Chuhan Cao, Pengfei Zhao, Jun Liu, Kang Computation and Language 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. |
| title | Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.08427 |