BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
Biomedical question answering often requires decisions from retrieved literature whose relevance, quality, and support for candidate answers are uneven. Most retrieval-augmented large language model (LLM) methods feed this literature to the model as flat text, leaving evidence reliability and remaining uncertainty largely implicit. We propose BELIEF, a structured evidence modeling and uncertainty-aware fusion framework for closed-set biomedical question answering. Rather than treating retrieved documents as undifferentiated context, BELIEF converts them into evidence objects that record clinical attributes, source quality, question relevance, support strength, and the associated candidate...
| Authors | Chang Zong, Hao Ning, Siliang Tang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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