@inproceedings{cai-etal-2026-lexrel,
    title = "{L}ex{R}el: Benchmarking Legal Relation Extraction for {C}hinese Civil Cases",
    author = "Cai, Yida  and
      Hu, Ranjuexiao  and
      Xie, Huiyuan  and
      Li, Chenyang  and
      Liu, Yun  and
      Ye, Yuxiao  and
      Liu, Zhenghao  and
      Shen, Weixing  and
      Liu, Zhiyuan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.980/",
    pages = "21440--21456",
    ISBN = "979-8-89176-390-6",
    abstract = "Legal relations serve as an important analytical framework for dispute resolution in civil cases. However, legal relations in Chinese civil cases remain underexplored in the field of legal AI, largely due to the absence of comprehensive schemas. In this work, we first introduce a comprehensive schema for legal relations in civil cases, which contains a hierarchical taxonomy and definitions of arguments. Based on this schema, we formulate a legal relation extraction task and present **LexRel**, an expert-annotated benchmark for legal relation extraction in the Chinese civil law domain. We use **LexRel** to evaluate state-of-the-art large language models (LLMs) on legal relation extraction, showing that current LLMs exhibit significant limitations in accurately identifying civil legal relations. Furthermore, we demonstrate that explicitly incorporating information about legal relations leads to promising performance gains on other downstream legal AI tasks."
}