Data Augmented Pipeline for Legal Information Extraction and Reasoning

Abstract: In this paper, we propose a pipeline leveraging 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 NLP tasks beyond the legal domain.