BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
What disease does this patient have? a large-scale open domain question answering dataset from medical exams
3 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
BiRG-LoRA achieves 69.31% macro-average accuracy across CMB, CMExam, MedQA, and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer trainable parameters under a matched Qwen3-8B protocol.
PPAI proposes prototype-based query-agent scoring and a multi-agent Bayesian game for P2P interoperability among personalized LLM agents on edge devices, claiming up to 7.96% accuracy gain and 16.34% latency reduction.
citing papers explorer
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BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
BELIEF improves closed-set biomedical QA by converting documents to structured evidence objects and fusing D-S symbolic belief estimation with LLM inference through reliability-aware arbitration.
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Clinically Structured Rank-Gated LoRA for Cross-Benchmark Medical Question Answering
BiRG-LoRA achieves 69.31% macro-average accuracy across CMB, CMExam, MedQA, and MedMCQA, outperforming MoELoRA by 0.89 points with 28.1% fewer trainable parameters under a matched Qwen3-8B protocol.
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PPAI: Enabling Personalized LLM Agent Interoperability for Collaborative Edge Intelligence
PPAI proposes prototype-based query-agent scoring and a multi-agent Bayesian game for P2P interoperability among personalized LLM agents on edge devices, claiming up to 7.96% accuracy gain and 16.34% latency reduction.