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.
Self-consistency improves chain of thought reasoning in language models,
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SRICL combines semantic retrieval from ESCO, in-context learning, fine-tuning, and output verification to achieve higher STRICT-F1 scores and fewer invalid or hallucinated skill spans than GPT-3.5 baselines on six public job-ad datasets.
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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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Job Skill Extraction via LLM-Centric Multi-Module Framework
SRICL combines semantic retrieval from ESCO, in-context learning, fine-tuning, and output verification to achieve higher STRICT-F1 scores and fewer invalid or hallucinated skill spans than GPT-3.5 baselines on six public job-ad datasets.