Fine-tuned BERT Large Uncased reaches 100% accuracy on a small, template-heavy Healthline-derived medical question classification dataset, while LoRA-tuned RoBERTa-large reaches only 78%.
KIMedQA: towards building knowledge - enhanced medical QA models
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A Comprehensive Study on Fine-Tuning Large Language Models for Medical Question Answering Using Classification Models and Comparative Analysis
Fine-tuned BERT Large Uncased reaches 100% accuracy on a small, template-heavy Healthline-derived medical question classification dataset, while LoRA-tuned RoBERTa-large reaches only 78%.