BiRG-LoRA reaches 69.31% macro-average accuracy across CMB, CMExam, MedQA and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer parameters under a matched single-seed protocol.
MING-MOE: Enhancing medical multi-task learning in large language models with sparse mixture of low-rank adapter experts,
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TriageRA-CCF combines source-side confidence, coverage, and counterfactual signals to supervise an adaptive LoRA rank router, reporting modest average accuracy gains over LoRA/DoRA/MoELoRA baselines on two 8B models under matched training.
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Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
BiRG-LoRA reaches 69.31% macro-average accuracy across CMB, CMExam, MedQA and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer parameters under a matched single-seed protocol.
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TriageRA-CCF: Source-Side Clinical Confidence and Coverage Signals for Adaptive Rank Budgeting in Medical LLMs
TriageRA-CCF combines source-side confidence, coverage, and counterfactual signals to supervise an adaptive LoRA rank router, reporting modest average accuracy gains over LoRA/DoRA/MoELoRA baselines on two 8B models under matched training.