aiqbench analysis
The paper compares 13 "decision models" against 15 generative LLM configurations on 1,273 MedQA questions. Generative setups reached higher peak accuracy (up to 96.86% vs. 89.08%), but decision models were cheaper and faster. The authors also stress that calibration and repeatability matter: some high-confidence answers were still wrong 1.71% to 12.77% of the time, so accuracy alone is an incomplete basis for medical deployment.
Benchmarking decision models against generative large language models on medical multiple-choice questions
arXiv:2610.38448v1 Announce Type: cross Abstract: Medical workflows often require selecting among predefined options. We compared 13 decision models with 15 generative large language model configurations, including different reasoning settings, using 1,273 four-option MedQA questions under text-only conditions. Accuracy ranged from 67.71% to 89.08% for decision models and from 78.32% to 96.86% for generative configurations. Among the tested configurations, Mercury Decide had the lowest mean final-attempt cost (USD 0.0069 per 1,000 questions), while Clef Omni had the shortest median request duration (0.291 seconds). Decision-model probabilities distinguished correct from incorrect answers, but error rates among answers assigned probabilities of at least 0.90 ranged from 1.71% to 12.77%. Answer agreement across three runs ranged from 92.30% to 100%, while probability repeatability varied. Decision models offered resource advantages alongside lower peak accuracy; selecting models for medical workflows requires considering probability quality and repeatability together with accuracy and the resources needed to detect and correct errors.
Abstract:Medical workflows often require selecting among predefined options. We compared 13 decision models with 15 generative large language model configurations, including different reasoning settings, using 1,273 four-option MedQA questions under text-only conditions. Accuracy ranged from 67.71% to 89.08% for decision models and from 78.32% to 96.86% for generative configurations. Among the tested configurations, Mercury Decide had the lowest mean final-attempt cost (USD 0.0069 per 1,000 questions), while Clef Omni had the shortest median request duration (0.291 seconds). Decision-model probabilities distinguished correct from incorrect answers, but error rates among answers assigned probabilities of at least 0.90 ranged from 1.71% to 12.77%. Answer agreement across three runs ranged from 92.30% to 100%, while probability repeatability varied. Decision models offered resource advantages alongside lower peak accuracy; selecting models for medical workflows requires considering probability quality and repeatability together with accuracy and the resources needed to detect and correct errors.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2610.38448 [cs.CL]
(or arXiv:2610.38448v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.38448 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 242 words