RegExtract: A Benchmark for PFAS Regulatory Information Extraction with LLMs

Extracting structured information from regulatory documents is a critical but challenging task: relevant facts are scattered across long, legally dense texts, and traceability of extracted facts to their source is essential for verification. With increasing awareness of the environmental and health impacts of per- and polyfluoroalkyl substances (PFAS), regulatory documents have expanded significantly, varying in scope and product relevance, making interpretation challenging for manufacturers and policymakers. We introduce \textbf{RegExtract}, the first benchmark for structured information extraction from PFAS regulatory documents, consisting of 976 human annotations across 20 US state documents and 46 regulations, with every annotation paired with a structured value and a provenance span. We define a typed, multi-field schema targeting key elements such as material definitions, permitted uses, and product exemptions, and propose provenance-aware evaluation metrics tailored to regulatory text. Through experiments with open- and closed-source LLMs across prompting strategies, document condensing methods, and schema partitioning approaches, we find that document identification fields are the main shortcoming of current LLMs, and that field-specific few-shot examples improve extraction. This work lays the groundwork for AI tools for hazardous substance identification, question answering, and compliance monitoring.