根据临床场景动态调整低秩适配器，提升医学多选题回答准确率
Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
- 用双轴门控机制按问题类型选择性激活低秩参数
- 跨4个医学数据集平均准确率达69.31%，优于现有方法
- 参数量减少28.1%且对标签噪声不敏感，适合临床部署
医学多选题问答需在异构知识领域与推理任务间实现参数高效适配。药物、诊断、公共卫生和护理类问题需不同低秩更新，部分记忆类题目应保持基础模型表征。本文提出BiRG-LoRA，一种单适配器的分层门控低秩适配方法：每层保留一个LoRA模块，但其秩维度由输入条件决定——针对每个问题，双轴门控结合隐藏语义证据与专业/职业先验、临床操作先验及其交互，选出稀疏的top-k秩原子子集，并通过标量注入系数控制更新强度。在匹配的Qwen3-8B CMB源协议下，BiRG-LoRA在所有可训练PEFT基线与匹配路由控制中取得最高四基准宏平均准确率：在CMB、CMExam、MedQA和MedMCQA上平均达69.31%。相比MoELoRA提升0.89个百分点，同时减少28.1%可训练参数；经基准分层的配对自举检验，该提升的95%置信区间为[0.42, 1.37]。基础对照显示，其较vanilla LoRA r16和主动秩匹配的LoRA r4分别提升0.83宏点；评估时弱轴扰动测试表明性能对中等标签噪声不敏感。结果支持有限结论：在统一种子协议下，临床结构化的秩分配可提升跨基准医学问答表现，训练种子方差仍为未来工作。
原文摘要 · Abstract (English)
Medical multiple-choice question answering requires parameter-efficient adaptation across heterogeneous knowledge domains and reasoning operations. A medication question, a diagnostic decision, a public-health item, and a nursing-action item may require different low-rank updates, while some recall items should preserve the base model's representation with only mild adapter intervention. We propose BiRG-LoRA, a single-adapter rank-gated LoRA method for medical question answering. BiRG-LoRA keeps one LoRA module per target layer but makes its rank dimension input-conditioned: for each question, a biaxial gate combines hidden semantic evidence with specialty/profession priors, clinical-operation priors, and their interaction to select a sparse top-$k$ subset of rank atoms. A scalar injection coefficient further controls the strength of the selected adapter update. Under a matched Qwen3-8B CMB-source protocol, BiRG-LoRA achieves the highest four-benchmark macro-average accuracy among trainable PEFT baselines and matched routing controls: 69.31% averaged over CMB, CMExam, MedQA, and MedMCQA. It improves over MoELoRA by 0.89 percentage points while using 28.1% fewer trainable parameters; a paired, benchmark-stratified bootstrap over final predictions gives a 95% confidence interval of [0.42, 1.37] for this macro-average gain. Basic controls show that BiRG-LoRA also improves over vanilla LoRA r16 and active-rank-matched LoRA r4 by 0.83 macro points, and an evaluation-time weak-axis perturbation check suggests that performance is not brittle to moderate tag noise. The results support a bounded claim: clinically structured rank allocation improves cross-benchmark medical QA under a matched single-seed protocol, while training-seed variance remains future work.
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