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arXiv · 2506.12182

Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs

Chenqian Le·Ziheng Gong·Chihang Wang·Haowei Ni·Panfeng Li·Xupeng Chen
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Abstract

Large language models (LLMs) have shown great potential in medical question answering (MedQA), yet adapting them to biomedical reasoning remains challenging due to domain-specific complexity and limited supervision. In this work, we study how prompt design and lightweight fine-tuning affect the performance of open-source LLMs on PubMedQA, a benchmark for multiple-choice biomedical questions. We focus on two widely used prompting strategies - standard instruction prompts and Chain-of-Thought (CoT) prompts - and apply QLoRA for parameter-efficient instruction tuning. Across multiple model families and sizes, our experiments show that CoT prompting alone can improve reasoning in zero-shot settings, while instruction tuning significantly boosts accuracy. However, fine-tuning on CoT prompts does not universally enhance performance and may even degrade it for certain larger models. These findings suggest that reasoning-aware prompts are useful, but their benefits are model- and scale-dependent. Our study offers practical insights into combining prompt engineering with efficient finetuning for medical QA applications.

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Chenqian Le, Ziheng Gong, Chihang Wang, Haowei Ni, Panfeng Li, Xupeng Chen. 2025-06-13. Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs. https://doi.org/10.1109/icahn67688.2025.00016

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