@inproceedings{chen-etal-2025-language,
    title = "How do Language Models Reshape Entity Alignment? A Survey of {LM}-Driven {EA} Methods: Advances, Benchmarks, and Future",
    author = "Chen, Zerui  and
      Fan, Huiming  and
      Wang, Qianyu  and
      He, Tao  and
      Liu, Ming  and
      Chang, Heng  and
      Yu, Weijiang  and
      Li, Ze  and
      Qin, Bing",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1184/",
    pages = "23230--23245",
    ISBN = "979-8-89176-332-6",
    abstract = "Entity alignment (EA), critical for knowledge graph (KG) integration, identifies equivalent entities across different KGs. Traditional methods often face challenges in semantic understanding and scalability. The rise of language models (LMs), particularly large language models (LLMs), has provided powerful new strategies. This paper systematically reviews LM-driven EA methods, proposing a novel taxonomy that categorizes methods in three key stages: data preparation, feature embedding, and alignment. We further summarize key benchmarks, evaluation metrics, and discuss future directions. This paper aims to provide researchers and practitioners with a clear and comprehensive understanding of how language models reshape the field of entity alignment."
}