印度结构性法律文书生成：一种模型无关的包装方法与VidhikDastaavej
Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej
AI总结：
本文提出VidhikDastaavej数据集和Model-Agnostic Wrapper框架，用于解决印度私人文书结构生成问题，提升事实准确性、连贯性和完整性。
摘要：
自动化法律文书起草可以提高效率并减轻手动法律工作的负担。然而，私人文书的结构生成仍处于探索阶段，特别是在印度，由于公共数据集稀缺和长格式法律起草适应模型的复杂性。为此，我们引入了VidhikDastaavej，一个由印度律师事务所合作编纂的大规模匿名私人文书数据集，涵盖133种多样类别。我们进一步提出Model-Agnostic Wrapper（MAW），一种两阶段生成框架，首先规划法律草案的章节结构，然后使用检索提示生成每个章节。MAW不依赖特定LLM，可适用于开源和闭源模型。全面评估显示，MAW在事实准确性、连贯性和完整性方面优于微调基线。本工作建立了新的基准数据集和可推广的生成框架，为AI辅助法律起草的未来研究铺平道路。
英文摘要：
Automating legal document drafting can improve efficiency and reduce the burden of manual legal work. Yet, the structured generation of private legal documents remains underexplored, particularly in the Indian context, due to the scarcity of public datasets and the complexity of adapting models for long-form legal drafting. To address this gap, we introduce VidhikDastaavej, a large-scale, anonymized dataset of private legal documents curated in collaboration with an Indian law firm. Covering 133 diverse categories, this dataset is the first resource of its kind and provides a foundation for research in structured legal text generation and Legal AI more broadly. We further propose a Model-Agnostic Wrapper (MAW), a two-stage generation framework that first plans the section structure of a legal draft and then generates each section with retrieval-based prompts. MAW is independent of any specific LLM, making it adaptable across both open- and closed-source models. Comprehensive evaluation, including lexical, semantic, LLM-based, and expert-driven assessments with inter-annotator agreement, shows that the wrapper substantially improves factual accuracy, coherence, and completeness compared to fine-tuned baselines. This work establishes both a new benchmark dataset and a generalizable generation framework, paving the way for future research in AI-assisted legal drafting.
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。
