Automated Argument Generation from Legal Facts
This work aims to help legal professionals by improving efficiency in case analysis, though it appears incremental as it applies existing LLMs to a specific domain.
The study tackled the problem of automating argument generation from legal facts to address the backlog of pending cases, achieving an average 63% overlap with gold standard annotations in experiments.
The count of pending cases has shown an exponential rise across nations (e.g., with more than 10 million pending cases in India alone). The main issue lies in the fact that the number of cases submitted to the law system is far greater than the available number of legal professionals present in a country. Given this worldwide context, the utilization of AI technology has gained paramount importance to enhance the efficiency and speed of legal procedures. In this study we partcularly focus on helping legal professionals in the process of analyzing a legal case. Our specific investigation delves into harnessing the generative capabilities of open-sourced large language models to create arguments derived from the facts present in legal cases. Experimental results show that the generated arguments from the best performing method have on average 63% overlap with the benchmark set gold standard annotations.