用强化学习提升印度法律判决预测与摘要能力
ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India
- 结合指令微调与AI反馈强化学习，用PPO优化法律推理
- 在判决预测和文书摘要任务中表现优于基线但不及专有模型
- 揭示法律文本中奖励对齐与领域适配的关键挑战
本文首次探索强化学习在印度法律场景中的应用。提出ReGal框架，融合多任务指令微调与基于AI反馈的强化学习（RLAIF），采用近端策略优化（PPO）进行训练。在两个核心法律任务上评估：（i）法院判决预测与解释（CJPE），（ii）法律文档摘要。尽管在标准指标上表现弱于监督学习与专有模型，但揭示了法律文本中奖励模型对齐、语言复杂性及领域适配等关键挑战。通过实证与定性分析，展示了强化学习在高风险、长文档法律任务中的可重构潜力。研究为未来优化法律推理流程提供基础，对构建可解释、自适应的法律AI系统具广泛意义。
原文摘要 · Abstract (English)
This paper presents an early exploration of reinforcement learning methodologies for legal AI in the Indian context. We introduce Reinforcement Learning-based Legal Reasoning (ReGal), a framework that integrates Multi-Task Instruction Tuning with Reinforcement Learning from AI Feedback (RLAIF) using Proximal Policy Optimization (PPO). Our approach is evaluated across two critical legal tasks: (i) Court Judgment Prediction and Explanation (CJPE), and (ii) Legal Document Summarization. Although the framework underperforms on standard evaluation metrics compared to supervised and proprietary models, it provides valuable insights into the challenges of applying RL to legal texts. These challenges include reward model alignment, legal language complexity, and domain-specific adaptation. Through empirical and qualitative analysis, we demonstrate how RL can be repurposed for high-stakes, long-document tasks in law. Our findings establish a foundation for future work on optimizing legal reasoning pipelines using reinforcement learning, with broader implications for building interpretable and adaptive legal AI systems.
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