Preprint
Collaboration among Multiple Large Language Models for Medical Question Answering
22 May 2025
Abstract
Empowered by vast internal knowledge reservoir, the new generation of large
language models (LLMs) demonstrate untapped potential to tackle medical tasks.
However, there is insufficient effort made towards summoning up a synergic
effect from multiple LLMs' expertise and background. In this study, we propose
a multi-LLM collaboration framework tailored on a medical multiple-choice
questions dataset. Through post-hoc analysis on 3 pre-trained LLM participants,
our framework is proved to boost all LLMs reasoning ability as well as
alleviate their divergence among questions. We also measure an LLM's confidence
when it confronts with adversary opinions from other LLMs and observe a
concurrence between LLM's confidence and prediction accuracy.
Metrics
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Details
- Title
- Collaboration among Multiple Large Language Models for Medical Question Answering
- Creators
- Kexin ShangChia-Hsuan ChangChristopher C Yang
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science; College of Computing and Informatics
- Other Identifier
- 991022054333304721