@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://preview.aclanthology.org/ingestion-acl-25/2025.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."
}