@inproceedings{chen-etal-2025-cracking,
    title = "Cracking Factual Knowledge: A Comprehensive Analysis of Degenerate Knowledge Neurons in Large Language Models",
    author = "Chen, Yuheng  and
      Cao, Pengfei  and
      Chen, Yubo  and
      Wang, Yining  and
      Liu, Shengping  and
      Liu, Kang  and
      Zhao, Jun",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.505/",
    pages = "10240--10261",
    ISBN = "979-8-89176-251-0",
    abstract = "Knowledge neuron theory provides a key approach to understanding the mechanisms of factual knowledge in Large Language Models (LLMs), which suggests that facts are stored within multi-layer perceptron neurons. This paper further explores **Degenerate Knowledge Neurons** (DKNs), where distinct sets of neurons can store identical facts, but unlike simple redundancy, they also participate in storing other different facts. Despite the novelty and unique properties of this concept, it has not been rigorously defined and systematically studied. Our contributions are: (1) We pioneer the study of structures in knowledge neurons by analyzing weight connection patterns, providing a comprehensive definition of DKNs from both functional and structural aspects. (2) Based on this definition, we develop the **Neuronal Topology Clustering** method, leading to a more accurate DKN identification. (3) We demonstrate the practical applications of DKNs in two aspects: guiding LLMs to learn new knowledge and relating to LLMs' robustness against input errors."
}