← 研踪 PaperTrailarXiv:2606.04262 · v2
Can I Take Another Dose? DoseBench: Evaluating LLM Decision-Making in OTC Dosing QA
首次提交 2026-06-03 06:30
版本更新 2026-10-29 08:02
雷达观测 2026-10-30 11:25
摘要
Large language models (LLMs) are increasingly used to answer everyday health questions online, including whether someone can take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in medical QA. Answering these questions requires checking previous doses, timing, and product-specific dosing limits, while recognizing when important information is missing. We introduce DoseBench, a focused benchmark of 230 consumer-style acetaminophen and ibuprofen dosing questions with human-annotated YES, NO, and AMBIGUOUS labels, and evaluate eight LLMs with five responses per question. Overall accuracy ranges from 44.8% to 62.2%, while models incorrectly approve 14.0% to 60.5% of prohibited doses. Models with similar accuracy can therefore make very different dosing decisions. Additional analyses show that these failures are not easily fixed by giving models more guidance. Asking models to check dose, timing, and missing information makes them more cautious, while providing drug facts improves accuracy but can make them overlook missing information. These findings highlight a broader challenge in health QA: applying rules correctly and recognizing when information is missing.
作者备注：12 pages, 5 figures
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-10-29 08:02 · 最新观测版本
Can I Take Another Dose? DoseBench: Evaluating LLM Decision-Making in OTC Dosing QA
Large language models (LLMs) are increasingly used to answer everyday health questions online, including whether someone can take another dose of an over-the-counter (OTC) medication. Yet this common safety-relevant setting remains underexplored in medical QA. Answering these questions requires checking previous doses, timing, and product-specific dosing limits, while recognizing when important information is missing. We introduce DoseBench, a focused benchmark of 230 consumer-style acetaminophen and ibuprofen dosing questions with human-annotated YES, NO, and AMBIGUOUS labels, and evaluate eight LLMs with five responses per question. Overall accuracy ranges from 44.8% to 62.2%, while models incorrectly approve 14.0% to 60.5% of prohibited doses. Models with similar accuracy can therefore make very different dosing decisions. Additional analyses show that these failures are not easily fixed by giving models more guidance. Asking models to check dose, timing, and missing information makes them more cautious, while providing drug facts improves accuracy but can make them overlook missing information. These findings highlight a broader challenge in health QA: applying rules correctly and recognizing when information is missing.
查看此版本解读 →查看此版本原文 ↗