@inproceedings{liu-etal-2025-legal,
    title = "Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction",
    author = "Liu, Junkai  and
      Tong, Yujie  and
      Huang, Hui  and
      Zheng, Bowen  and
      Hu, Yiran  and
      Wu, Peicheng  and
      Xiao, Chuan  and
      Onizuka, Makoto  and
      Yang, Muyun  and
      Zheng, Shuyuan",
    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.322/",
    pages = "6345--6360",
    ISBN = "979-8-89176-332-6",
    abstract = "Legal judgment prediction (LJP), which enables litigants and their lawyers to forecast judgment outcomes and refine litigation strategies, has emerged as a crucial legal NLP task. Existing studies typically utilize legal facts, i.e., facts that have been established by evidence and determined by the judge, to predict the judgment. However, legal facts are often difficult to obtain in the early stages of litigation, significantly limiting the practical applicability of fact-based LJP. To address this limitation, we propose a novel legal NLP task: \textit{legal fact prediction} (LFP), which takes the evidence submitted by litigants for trial as input to predict legal facts, thereby empowering fact-based LJP technologies to make predictions in the absence of ground-truth legal facts. We also propose the first benchmark dataset, LFPBench, for evaluating the LFP task. Our extensive experiments on LFPBench demonstrate the effectiveness of LFP-empowered LJP and highlight promising research directions for LFP."
}