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

ACL ARR 2025 May Submission6432 Authors

Abstract: Automating legal document drafting can enhance efficiency, reduce manual workload, and streamline legal workflows. However, the structured generation of private legal documents remains underexplored, particularly in the Indian legal context due to limited public data and model adaptation challenges. We propose a Model-Agnostic Wrapper (MAW), a flexible, two-stage generation framework that first produces section titles and then generates section-wise content using retrieval-based prompts. This wrapper decouples generation from any specific model, enabling compatibility with a range of open- and closed-source LLMs, and ensuring coherence, factual alignment, and reduced hallucination. To enable practical use, we build a Human-in-the-Loop Document Generation System, an interactive interface where users can input document types, refine sections, and iteratively generate structured drafts. The tool supports real-world legal workflows and will be made publicly accessible upon acceptance with privacy and security safeguards. Comprehensive evaluations, including expert-based assessments, demonstrate that the wrapper-based approach substantially improves document quality over baseline and fine-tuned models. Our framework establishes a scalable and adaptable path toward structured AI-assisted legal drafting in the Indian domain.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Resources and Evaluation, NLP Applications, Machine Learning for NLP, Language Modeling, Interpretability and Analysis of Models for NLP, Generation, Human-Centered NLP

Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English

Submission Number: 6432