Data Augmented Pipeline for Legal Information Extraction and Reasoning
arXiv:2601.05609
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.
Accepted in the Demonstration Track at ICAIL 2025