用证据预测法律事实，让判决预测更早可用
Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction
- 从诉讼提交的证据出发，预测法官最终认定的事实
- 在真实数据集上验证，使判决预测提前至诉讼初期
- 适合法律AI研究者和智能法律系统开发者
法律判决预测（LJP）作为法律自然语言处理的关键任务，可帮助当事人和律师预判案件结果并优化诉讼策略。现有研究多依赖已由证据确认且由法官裁定的法律事实进行预测，但在诉讼早期这些事实往往难以获取，严重限制了其实际应用。为此，本文提出全新任务：法律事实预测（LFP），输入为当事人提交的证据，输出为可能被认定的法律事实，从而支持基于事实的判决预测在无真实事实时开展。同时，本文构建首个基准数据集LFPBench用于评估该任务。在该数据集上的大量实验表明，赋能LFP后的判决预测效果显著，揭示了该方向的重要研究潜力。
原文摘要 · Abstract (English)
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: 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.
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