@inproceedings{wu-etal-2025-kmatrix,
    title = "{KM}atrix-2: A Comprehensive Heterogeneous Knowledge Collaborative Enhancement Toolkit for Large Language Model",
    author = "Wu, Shun  and
      Wu, Di  and
      Sun, Wangtao  and
      Huang, Ziyang  and
      Yuan, Xiaowei  and
      Luo, Kun  and
      Zhang, XueYou  and
      He, Shizhu  and
      Zhao, Jun  and
      Liu, Kang",
    editor = {Habernal, Ivan  and
      Schulam, Peter  and
      Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-demos.42/",
    pages = "591--601",
    ISBN = "979-8-89176-334-0",
    abstract = "The paper presents KMatrix-2, an open-source toolkit that supports comprehensive heterogeneous knowledge collaborative enhancement for Large Language Models (LLMs). As the successor of KMatrix, our toolkit offers powerful modular components and typical enhancement patterns for convenient construction of mainstream knowledge-enhanced LLMs systems. Besides, it provides unified knowledge integration and joint knowledge retrieval methods to achieve more comprehensive heterogeneous knowledge collaborative enhancement. Compared with KMatrix which mainly focuses on descriptive knowledge, this work additionally considers procedural knowledge. Moreover, systematic inter-context and context-memory knowledge conflict resolution methods are offered for better knowledge integration. Some key research questions in heterogeneous knowledge-enhanced Large Language Models systems are analyzed, and our toolkit{'}s capability in building such systems is validated."
}