用对抗自博弈提升律师论证，改善法律判决预测的偏倚与罕见案例表现。
ASP2LJ : An Adversarial Self-Play Laywer Augmented Legal Judgment Framework
- 引入对抗自博弈机制，让律师角色自我迭代优化论证策略。
- 在模拟法院数据集上提升判决预测准确率，罕见案例集含120个尾部案例。
- 开源数据与代码，适合法律AI研究者与司法智能化开发者使用。
法律判决预测（LJP）旨在预测判决结果，包括罪名、刑期与罚金，是大语言模型中的关键环节。当前面临两大挑战：（1）长尾分布问题——真实案件数据因人工标注成本高且分布不均，导致模型性能下降；（2）律师作用被忽视——现有系统聚焦于辅助法官决策，却未充分考虑律师在完善论据中的关键作用，限制了整体判决准确性。为此，我们提出对抗自博弈律师增强型法律判决框架ASP2LJ，融合案例生成模块以缓解长尾分布，并引入对抗自博弈机制提升律师论证能力。该框架使法官可参考演化后的律师论据，从而提升判决的客观性、公平性与合理性。此外，我们构建了针对中国罕见法律案例的RareCases数据集，包含120个尾部案例。实验在SimuCourt和RareCases数据集上验证了方法有效性，结果表明框架显著提升预测性能。本工作贡献包括一个集成框架、一个罕见案例数据集，并公开发布数据与代码，推动自动化司法系统研究。
原文摘要 · Abstract (English)
Legal Judgment Prediction (LJP) aims to predict judicial outcomes, including relevant legal charge, terms, and fines, which is a crucial process in Large Language Model(LLM). However, LJP faces two key challenges: (1)Long Tail Distribution: Current datasets, derived from authentic cases, suffer from high human annotation costs and imbalanced distributions, leading to model performance degradation. (2)Lawyer's Improvement: Existing systems focus on enhancing judges' decision-making but neglect the critical role of lawyers in refining arguments, which limits overall judicial accuracy. To address these issues, we propose an Adversarial Self-Play Lawyer Augmented Legal Judgment Framework, called ASP2LJ, which integrates a case generation module to tackle long-tailed data distributions and an adversarial self-play mechanism to enhance lawyers' argumentation skills. Our framework enables a judge to reference evolved lawyers' arguments, improving the objectivity, fairness, and rationality of judicial decisions. Besides, We also introduce RareCases, a dataset for rare legal cases in China, which contains 120 tail-end cases. We demonstrate the effectiveness of our approach on the SimuCourt dataset and our RareCases dataset. Experimental results show our framework brings improvements, indicating its utilization. Our contributions include an integrated framework, a rare-case dataset, and publicly releasing datasets and code to support further research in automated judicial systems.
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