Examining Long-Context Large Language Models for Environmental Review Document Comprehension

2024-07-10

AI Summary

The application of large language models (LLMs) to National Environmental Policy Act (NEPA) environmental reviews lacks empirical validation. Method: We introduce NEPAQuAD1.0—the first benchmark dataset for professional long-document understanding in this domain—and systematically evaluate five long-context LLMs (e.g., Claude, Gemini, GPT-4) on legal, technical, and compliance-oriented question answering. We propose a novel evaluation methodology that disentangles intrinsic knowledge from context-grounded reasoning, and conduct fine-grained analysis across closed-ended, open-ended, and problem-solving question types. Leveraging PDF parsing, retrieval-augmented generation (RAG), and prompt engineering, we assess model performance under realistic conditions. Contribution/Results: RAG substantially improves accuracy; models excel on Yes/No questions but struggle with problem-solving and open-ended reasoning. This work establishes a reproducible, empirically grounded evaluation framework for deploying LLMs in high-stakes, compliance-critical domains.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

As LLMs become increasingly ubiquitous, researchers have tried various techniques to augment the knowledge provided to these models. Long context and retrieval-augmented generation (RAG) are two such methods that have recently gained popularity. In this work, we examine the benefits of both of these techniques by utilizing question answering (QA) task in a niche domain. While the effectiveness of LLM-based QA systems has already been established at an acceptable level in popular domains such as trivia and literature, it has not often been established in niche domains that traditionally require specialized expertise. We construct the NEPAQuAD1.0 benchmark to evaluate the performance of five long-context LLMs -- Claude Sonnet, Gemini, GPT-4, Llama 3.1, and Mistral -- when answering questions originating from Environmental Impact Statements prepared by U.S. federal government agencies in accordance with the National Environmental Environmental Act (NEPA). We specifically measure the ability of LLMs to understand the nuances of legal, technical, and compliance-related information present in NEPA documents in different contextual scenarios. We test the LLMs' internal prior NEPA knowledge by providing questions without any context, as well as assess how LLMs synthesize the contextual information present in long NEPA documents to facilitate the question/answering task. We compare the performance of the models in handling different types of questions (e.g., problem-solving, divergent, etc.). Our results suggest that RAG powered models significantly outperform those provided with only the PDF context in terms of answer accuracy, regardless of the choice of the LLM. Our further analysis reveals that many models perform better answering closed type questions (Yes/No) than divergent and problem-solving questions.

Problem

• Evaluating LLMs for NEPA regulatory reasoning tasks
• Assessing LLM performance on environmental impact documents
• Testing LLMs' ability to process lengthy regulatory texts

Innovation

• First NEPA benchmark from EIS documents
• Modular evaluation pipeline for LLMs
• RAG outperforms long-context QA tasks