Legal Document Generation: Harnessing Large Language Models For Diverse Legal Workflows

Shree Shalini R, Aeries Research & Innovation, Aeries Technology

Dr. Jai Vishwakarma, Aeries Research & Innovation, Aeries Technology

ABSTRACT

Creating legal documents like Statement of Work (SoW) Documents, and process documents like Procurement, Treasury, Accounts Payable, Intangible Assets under Development (IAUD), and policies, is a laborious and time-consuming process, which often takes weeks to complete. After the introduction of Large Language Model (LLM), people in the Legal domain have started using it for various tasks. But it is important to note that there is a gap in using LLM’s for Automated Document Generation. This paper comes to fill in that gap between the usage of LLMs in various Auto Document generation following certain structures to be compliant. We have used a pioneering approach to automate the generation of diverse legal documents, utilizing the liberty of using the advanced capabilities of LLMs. To craft and generate documents compliant with certain structures and legal criteria, we have designed our methodology that integrates multi-agent systems with iterative prompt refinement. Our innovative framework includes features like specialized agents for semantics identification, information retrieval, and content generation which aims at minimizing user intervention at the same time ensuring document precision and operational efficiency. Through real-world testing, we demonstrate the efficacy of our approach in semi-structured document generation across real-world contexts. To close this gap between the capabilities of LLM and automated legal document generation, our work contributes to the progressive dialogue on LLMs in legal domains and at the same time also holds high importance for revolutionizing the workflows in the legal domain. Our automated document generation solution specifically designed for Legal helps a lot of professionals to drastically reduce the time and manual effort involved in creating legal documents from scratch, thereby significantly improving efficiency and productivity across legal domains.

Keywords: Automated Legal Document Generation, Large Language Models, Statements of Work, NLP, Legal Document scrutinizing.