Generating Legal Arguments using LLM and Vector Database to Support Precedents

Legal precedents and prior cases play a pivotal role in forming legal arguments and decisions for new cases. However, manually searching and identifying relevant precedents is quite a back-breaking task. This research paper delves into the process of synthesizing legal arguments for contemporary cases by leveraging historical precedents and case law, all of which is integrated into a pre-trained Large Language Model (LLM) to enhance its capabilities and give out domain specific knowledge. We propose an approach that utilizes LLM architecture and a vector database to analyze a corpus of legal documents and extract relevant arguments from past cases. These extracted arguments and their textual features are then used as references for our argument generation. For a new user input, our approach identifies similar past precedents and generates new arguments by adapting relevant extracted arguments from those precedents. The objective of this research is to leverage Large Language Models (LLMs) for the generation of legal arguments, thereby facilitating and bolstering legal analysis and decision-making processes. The envisioned applications of this implementation of Large Language Models (LLMs) encompass the drafting of legal arguments, identification of pertinent precedents, and the augmentation of human legal reasoning capabilities.