Data Augmented Pipeline for Legal Information Extraction and Reasoning
By: Nguyen Minh Phuong , Ha-Thanh Nguyen , May Myo Zin and more
Potential Business Impact:
Helps lawyers find important information faster.
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.
Similar Papers
Large Language Models Meet Legal Artificial Intelligence: A Survey
Computation and Language
Helps lawyers use smart computers for legal work.
Metadata Extraction Leveraging Large Language Models
Machine Learning (Stat)
Helps lawyers find important contract parts faster.
Augmenting Anonymized Data with AI: Exploring the Feasibility and Limitations of Large Language Models in Data Enrichment
Cryptography and Security
Keeps private information safe while still useful.