Automated Argument Generation from Legal Facts

Oscar Tuvey, Procheta Sen
10/9/2023
cs.AI

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Cite this paper

@article{tuvey2023automated,
  title  = {Automated Argument Generation from Legal Facts},
  author = {Oscar Tuvey and Procheta Sen},
  year   = {2023},
  eprint = {2310.05680},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.05680v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.05680.svg)](https://codeofpaper.com/paper/2310.05680)

Discussion