# Structured Legal Document Generation in India: A Model-Agnostic Wrapper Approach with VidhikDastaavej

arXiv: https://arxiv.org/abs/2504.03486 · HTML: https://mlsanity.com/paper/2504.03486/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 4 Apr 2025.

## Builds on (most cited first, 17 of 17)

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- [Fill in the BLANC: Human-free quality estimation of document summaries](https://mlsanity.com/paper/2002.09836/) (2020)
- [ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation](https://mlsanity.com/paper/2105.13562/) (2021)
- [Identification of Rhetorical Roles of Sentences in Indian Legal Judgments](https://mlsanity.com/paper/1911.05405/) (2019)
- [Semantic Segmentation of Legal Documents via Rhetorical Roles](https://mlsanity.com/paper/2112.01836/) (2021)
- [CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation](https://mlsanity.com/paper/2406.17186/) (2024)
- [Legal Judgment Reimagined: PredEx and the Rise of Intelligent AI Interpretation in Indian Courts](https://mlsanity.com/paper/2406.04136/) (2024)
- [Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach](https://mlsanity.com/paper/2312.09591/) (2023)
- [Legal Question-Answering in the Indian Context: Efficacy, Challenges, and Potential of Modern AI Models](https://mlsanity.com/paper/2309.14735/) (2023)
- [nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of Lexical and Semantic-based models](https://mlsanity.com/paper/2204.07853/) (2022)
- [HiCuLR: Hierarchical Curriculum Learning for Rhetorical Role Labeling of Legal Documents](https://mlsanity.com/paper/2409.18647/) (2024)
- [CoPERLex: Content Planning with Event-based Representations for Legal Case Summarization](https://mlsanity.com/paper/2501.14112/) (2025)
- [LexGenie: Automated Generation of Structured Reports for European Court of Human Rights Case Law](https://mlsanity.com/paper/2503.03266/) (2025)

## Abstract (arXiv)

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.