Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction

TL;DR

This paper introduces legal fact prediction (LFP), a new NLP task that predicts legal facts from evidence to improve legal judgment prediction when facts are unavailable, supported by a new benchmark dataset.

Contribution

It proposes the first LFP task and dataset, enabling fact-based legal judgment prediction without prior legal facts, advancing legal NLP research.

Findings

LFP improves judgment prediction accuracy

LFP-empowered LJP outperforms baseline models

LFPBench is effective for evaluating legal fact prediction

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: 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.