@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://aclanthology.org/2025.emnlp-main.322/",
doi = "10.18653/v1/2025.emnlp-main.322",
pages = "6334--6349",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction
%A Liu, Junkai
%A Tong, Yujie
%A Huang, Hui
%A Zheng, Bowen
%A Hu, Yiran
%A Wu, Peicheng
%A Xiao, Chuan
%A Onizuka, Makoto
%A Yang, Muyun
%A Zheng, Shuyuan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F liu-etal-2025-legal
%X 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.
%R 10.18653/v1/2025.emnlp-main.322
%U https://aclanthology.org/2025.emnlp-main.322/
%U https://doi.org/10.18653/v1/2025.emnlp-main.322
%P 6334-6349
Markdown (Informal)
[Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction](https://aclanthology.org/2025.emnlp-main.322/) (Liu et al., EMNLP 2025)
ACL
- Junkai Liu, Yujie Tong, Hui Huang, Bowen Zheng, Yiran Hu, Peicheng Wu, Chuan Xiao, Makoto Onizuka, Muyun Yang, and Shuyuan Zheng. 2025. Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 6334–6349, Suzhou, China. Association for Computational Linguistics.