Data Augmented Pipeline for Legal Information Extraction and Reasoning

TL;DR

This paper introduces a data augmentation pipeline using Large Language Models to improve legal information extraction, reducing manual effort and increasing system robustness across NLP tasks.

Contribution

It presents a simple, effective, and generalizable data augmentation method leveraging LLMs for legal and other NLP information extraction tasks.

Findings

Significant reduction in manual data annotation effort

Enhanced robustness of information extraction systems

Applicable to multiple NLP domains

Abstract

In this paper, we propose a pipeline leveraging Large Language Models (LLMs) for data augmentation in Information Extraction tasks within the legal domain. The proposed method is both simple and effective, significantly reducing the manual effort required for data annotation while enhancing the robustness of Information Extraction systems. Furthermore, the method is generalizable, making it applicable to various Natural Language Processing (NLP) tasks beyond the legal domain.