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Keyword: weighted majority vote ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study

   Yang, Li, Zhou et al. · Journal of Medical Internet Research · 2025

   The authors propose LLM-Synergy, two ways of combining several language models, and report that both scored above the individual models on three medical question-answering datasets.

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   1. UncheckedA boosting-weighted majority vote of LLMs scored 35.84% on MedMCQA, 96.21% on PubMedQA and 37.26% on MedQA-USMLE, versus the best single model.“Specifically comparing the best individual LLM, the Boosting-based Majority Weighted Vote achieved accuracies of 35.84% on MedMCQA (+3.81%), 96.21% on PubMedQA (+0.64%), and 37.26% (tie) on MedQA-USMLE.”
   2. UncheckedA method that picks the best language model for each medical question scored 38.01% on MedMCQA, 96.36% on PubMedQA and 38.13% on MedQA-USMLE.“The Cluster-based Dynamic Model Selection yields even higher accuracies of 38.01% (+5.98%) for MedMCQA, 96.36% (+1.09%) for PubMedQA, and 38.13% (+0.87%) for MedQA-USMLE.”