ReGal：基于PPO的法律AI在印度判决预测和摘要中的初步探索
ReGal: A First Look at PPO-based Legal AI for Judgment Prediction and Summarization in India
AI总结：
ReGal通过PPO结合多任务指令微调与强化学习，探索法律AI在印度判决预测和摘要中的应用，揭示了强化学习在法律文本中的挑战与潜力。
摘要：
本文介绍了在印度背景下对强化学习方法用于法律AI的早期探索。我们引入了基于强化学习的法律推理（ReGal）框架，该框架结合了多任务指令微调与通过人工智能反馈进行强化学习（RLAIF）的方法，使用近端策略优化（PPO）。我们的方法在两个关键法律任务上进行了评估：（i）法院判决预测与解释（CJPE），以及（ii）法律文档摘要。尽管该框架在标准评估指标上相比监督和专有模型表现不佳，但它为应用强化学习到法律文本中的挑战提供了有价值的见解。这些挑战包括奖励模型对齐、法律语言复杂性和领域特定的适应性。通过实证和定性分析，我们展示了如何将强化学习重新应用于高风险、长文档任务中的法律领域。我们的发现为未来优化法律推理管道使用强化学习奠定了基础，对构建可解释和适应性强的法律AI系统具有更广泛的意义。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
