构建印度法律事实判断数据集与模型，提升司法AI的准确性与可解释性。
TathyaNyaya and FactLegalLlama: Advancing Factual Judgment Prediction and Explanation in the Indian Legal Context
- 构建首个专注事实陈述的印度法律判案数据集TathyaNyaya。
- 基于该数据集微调出可生成合理解释的FactLegalLlama模型。
- 适合法律AI研究者与司法智能化系统开发者使用。
在基于事实的判决预测与解释（FJPE）领域，依赖真实事实数据是构建可靠、现实的AI决策工具的关键。本文提出TathyaNyaya，这是首个专为印度法律语境设计的大型标注数据集，涵盖印度最高法院及各高等法院的判决文本。该数据集名称源自印地语“Tathya”（事实）与“Nyaya”（正义），聚焦于事实陈述而非完整法律文本，更贴近真实司法实践中以事实驱动裁决的过程。为配合该数据集，我们推出了FactLegalLlama，一个针对LLaMa-3-8B大模型指令微调的变体，专门优化于生成高质量的判案解释。在TathyaNyaya的事实数据上微调后，FactLegalLlama兼具高预测准确率与上下文相关的连贯解释能力，满足司法AI系统对透明性与可解释性的关键需求。方法结合变压器模型进行二元判决预测，以及FactLegalLlama生成解释，形成面向印度法律领域的强大FJPE框架。TathyaNyaya不仅在规模与多样性上超越现有数据集，更确立了法律分析中可解释AI系统的基准。研究结果强调事实精确性与领域特化微调对提升预测性能与可解释性的关键作用，使TathyaNyaya与FactLegalLlama成为司法辅助决策的基石资源。
原文摘要 · Abstract (English)
In the landscape of Fact-based Judgment Prediction and Explanation (FJPE), reliance on factual data is essential for developing robust and realistic AI-driven decision-making tools. This paper introduces TathyaNyaya, the largest annotated dataset for FJPE tailored to the Indian legal context, encompassing judgments from the Supreme Court of India and various High Courts. Derived from the Hindi terms "Tathya" (fact) and "Nyaya" (justice), the TathyaNyaya dataset is uniquely designed to focus on factual statements rather than complete legal texts, reflecting real-world judicial processes where factual data drives outcomes. Complementing this dataset, we present FactLegalLlama, an instruction-tuned variant of the LLaMa-3-8B Large Language Model (LLM), optimized for generating high-quality explanations in FJPE tasks. Finetuned on the factual data in TathyaNyaya, FactLegalLlama integrates predictive accuracy with coherent, contextually relevant explanations, addressing the critical need for transparency and interpretability in AI-assisted legal systems. Our methodology combines transformers for binary judgment prediction with FactLegalLlama for explanation generation, creating a robust framework for advancing FJPE in the Indian legal domain. TathyaNyaya not only surpasses existing datasets in scale and diversity but also establishes a benchmark for building explainable AI systems in legal analysis. The findings underscore the importance of factual precision and domain-specific tuning in enhancing predictive performance and interpretability, positioning TathyaNyaya and FactLegalLlama as foundational resources for AI-assisted legal decision-making.
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