[2603.19539] FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment

Computer Science > Computation and Language arXiv:2603.19539 (cs) [Submitted on 20 Mar 2026] Title:FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment Authors:Betty Xiong, Jillian Fisher, Benjamin Newman, Meng Hu, Shivangi Gupta, Yejin Choi, Lanyan Fang, Russ B Altman View a PDF of the paper titled FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment, by Betty Xiong and 7 other authors View PDF HTML (experimental) Abstract:We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Administration (FDA) drug label documents. Drug labels contain rich but heterogeneous clinical and regulatory information, making accurate question answering difficult for current language models. In collaboration with FDA regulatory assessors, we introduce FDARxBench, and construct a multi-stage pipeline for generating high-quality, expert curated, QA examples spanning factual, multi-hop, and refusal tasks, and design evaluation protocols to assess both open-book and closed-book reasoning. Experiments across proprietary and open-weight models reveal substantial gaps in factual grounding, long-context retrieval, and safe refusal behavior. While motivated by FDA generic drug assessment needs, this benchmark also provides a substantial foundation for challenging regulatory-grade evaluation of label comprehens...