不微调模型，靠自信度路由实现医疗问答性能提升
CURE: Confidence-driven Unified Reasoning Ensemble Framework for Medical Question Answering
- 根据主模型自信度，动态调度辅助模型协作推理
- 在PubMedQA上达到95.0%准确率，MedMCQA达78.0%
- 适合算力有限的医疗机构快速部署高级医疗AI
高性能医疗大模型通常需大量微调与计算资源，限制了资源受限医疗机构的使用。本文提出一种基于自信度的多模型协同框架，无需微调即可提升医疗问答能力。框架采用两阶段设计：自信度检测模块评估主模型置信度，自适应路由机制将低置信度问题转发给知识互补的辅助模型进行协同推理。我们在Qwen3-30B-A3B-Instruct、Phi-4 14B和Gemma 2 12B上，于MedQA、MedMCQA和PubMedQA三个医学基准测试中验证该方法。结果表明，该框架表现优异，尤其在PubMedQA（95.0%）和MedMCQA（78.0%）上表现突出。消融实验确认，结合自信度感知路由与多模型协作显著优于单模型及均匀推理策略。本工作证明，策略性模型协同是提升医疗AI系统的可行、高效路径，对推动资源受限环境下先进医疗AI的普及具有重要意义。
原文摘要 · Abstract (English)
High-performing medical Large Language Models (LLMs) typically require extensive fine-tuning with substantial computational resources, limiting accessibility for resource-constrained healthcare institutions. This study introduces a confidence-driven multi-model framework that leverages model diversity to enhance medical question answering without fine-tuning. Our framework employs a two-stage architecture: a confidence detection module assesses the primary model's certainty, and an adaptive routing mechanism directs low-confidence queries to Helper models with complementary knowledge for collaborative reasoning. We evaluate our approach using Qwen3-30B-A3B-Instruct, Phi-4 14B, and Gemma 2 12B across three medical benchmarks; MedQA, MedMCQA, and PubMedQA. Result demonstrate that our framework achieves competitive performance, with particularly strong results in PubMedQA (95.0\%) and MedMCQA (78.0\%). Ablation studies confirm that confidence-aware routing combined with multi-model collaboration substantially outperforms single-model approaches and uniform reasoning strategies. This work establishes that strategic model collaboration offers a practical, computationally efficient pathway to improve medical AI systems, with significant implications for democratizing access to advanced medical AI in resource-limited settings.
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