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.
Are large lan- guage models really good logical reasoners? a comprehensive evaluation and beyond
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.
citing papers explorer
-
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.
-
Communication-Efficient Federated Fine-Tuning
FDA-Opt unifies and improves upon FedOpt and FDA for communication-efficient federated fine-tuning of language models on NLP tasks, outperforming optimized FedOpt baselines.