JMIR Publications

ABSTRACT

Background:

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, including medical question-answering (QA). However, individual LLMs often exhibit varying performance across different medical QA datasets. We benchmarked individual zero-shot LLMs (GPT-4, Llama2-13B, Vicuna-13B, MedLlama-13B, and MedAlpaca-13B) to assess their baseline performance. Within benchmark, GPT-4 achieves best 71% on MedMCQA, Vicuna-13B achieves 89.5% on PubMedQA, and MedAlpaca-13B achieves best 70% among all, showing the potential for better performance across different tasks and highlighting the need for strategies that can harness their collective strengths. Ensemble learning methods, combining multiple models to improve overall accuracy and reliability, offer a promising approach to address this challenge.

Objective:

To develop and evaluate efficient ensemble learning approaches, we focus on improving performance across three medical QA datasets through our proposed two ensemble strategies.

Methods:

Our study employs three medical QA datasets: PubMedQA (1,000 manually labeled + 11,269 test yes/no/maybe questions), MedQA-USMLE (12,724 English board-style questions; 1,272 test, five options), and MedMCQA (182,822 training / 4,183 test questions, four-option multiple-choice). We introduced the LLM-Synergy framework, consisting of two ensemble methods: (1) a Boosting-based Weighted Majority Vote ensemble, refining decision-making by adaptively weighting each LLM, and (2) a Cluster-based Dynamic Model Selection ensemble, dynamically selecting optimal LLMs for each query based on question-context embeddings and clustering.

Results:

Both ensemble methods outperformed individual LLMs across all three datasets. 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. The Cluster-based Dynamic Model Selection yields an even higher accuracies of 38.01% (+5.98%) for MedMCQA, 96.36% (+1.09%) for PubMedQA, and 38.13% (+0.87%) for MedQA-USMLE.

Conclusions:

The LLM-Synergy framework, utilizing two ensemble methods, represents a significant advancement in leveraging LLMs for medical QA tasks. Through effectively combining the strengths of diverse LLMs, this framework provides a flexible and efficient strategy adaptable to current and future challenges in biomedical informatics.