Data Augmented Pipeline for Legal Information Extraction and Reasoning
This addresses data scarcity in legal NLP tasks, though it appears incremental as it applies existing LLM techniques to a specific domain.
The paper tackles the problem of manual data annotation in legal information extraction by proposing a pipeline that uses Large Language Models for data augmentation, which reduces annotation effort and enhances system robustness.
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.