Leveraging Large Language Models for Information Retrieval from NEPA Documents

Abstract

This paper explores the application of large language models (LLMs) to efficiently and accurately extract relevant information from National Environmental Policy Act (NEPA) documents, specifically focusing on environmental impact statements (EIS). NEPA mandates federal agencies to evaluate the environmental effects of their proposed actions, and EIS documents are essential for this process. However, these documents are often lengthy and complex, making manual information extraction time-consuming and error-prone. We address this challenge by leveraging advanced natural language processing techniques and the newly introduced NEPAQuAD1.0 dataset, which contains 1,450 question-answer pairs generated under human supervision. Our approach involves fine-tuning the Meta-Llama-3.1-8B-Instruct model on this dataset. The results demonstrate significant improvements in retrieval accuracy and efficiency compared to baseline models, highlighting the potential of LLMs to streamline the environmental review process and provide valuable insights for environmental policy analysis. This work contributes to the broader field of natural language processing by offering a robust method for handling complex, domain-specific information retrieval tasks.