Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction
This addresses a practical problem for litigants and lawyers by making LJP applicable earlier in litigation, though it is incremental as it builds on existing LJP technologies.
The paper tackles the limitation of legal judgment prediction (LJP) by introducing legal fact prediction (LFP), a new task that predicts legal facts from evidence to enable LJP without ground-truth facts, and validates it with a benchmark dataset and experiments.
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.