# Instruction Tuning and CoT Prompting for Contextual Medical QA with LLMs

arXiv: https://arxiv.org/abs/2506.12182 · HTML: https://mlsanity.com/paper/2506.12182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 41 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 30 of 32)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [PubMedQA: A Dataset for Biomedical Research Question Answering](https://mlsanity.com/paper/1909.06146/) (2019)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text](https://mlsanity.com/paper/2403.18421/) (2024)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaboration](https://mlsanity.com/paper/2411.15692/) (2024)
- [Contextual Hourglass Network for Semantic Segmentation of High Resolution Aerial Imagery](https://mlsanity.com/paper/1810.12813/) (2018)
- [ComposerX: Multi-Agent Symbolic Music Composition with LLMs](https://mlsanity.com/paper/2404.18081/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Harnessing Earnings Reports for Stock Predictions: A QLoRA-Enhanced LLM Approach](https://mlsanity.com/paper/2408.06634/) (2024)
- [RAG-RLRC-LaySum at BioLaySumm: Integrating Retrieval-Augmented Generation and Readability Control for Layman Summarization of Biomedical Texts](https://mlsanity.com/paper/2405.13179/) (2024)
- [Enhance Image-to-Image Generation with LLaVA-generated Prompts](https://mlsanity.com/paper/2406.01956/) (2024)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models](https://mlsanity.com/paper/2412.19925/) (2024)
- [SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction](https://mlsanity.com/paper/2408.05696/) (2024)
- [Adaptive Fault Tolerance Mechanisms of Large Language Models in Cloud Computing Environments](https://mlsanity.com/paper/2503.12228/) (2025)
- [Prediction of COVID-19 Patients' Emergency Room Revisit using Multi-Source Transfer Learning](https://mlsanity.com/paper/2306.17257/) (2023)
- [Research on Large Language Model Cross-Cloud Privacy Protection and Collaborative Training based on Federated Learning](https://mlsanity.com/paper/2503.12226/) (2025)
- [Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration](https://mlsanity.com/paper/2504.00950/) (2025)
- [Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing](https://mlsanity.com/paper/2505.11743/) (2025)
- [Cross-Cloud Data Privacy Protection: Optimizing Collaborative Mechanisms of AI Systems by Integrating Federated Learning and LLMs](https://mlsanity.com/paper/2505.13292/) (2025)
- [BioMamba: Domain-Adaptive Biomedical Language Models](https://mlsanity.com/paper/2408.02600/) (2024)
- [NeRF-Based defect detection](https://mlsanity.com/paper/2504.00270/) (2025)
- [Avocado Price Prediction Using a Hybrid Deep Learning Model: TCN-MLP-Attention Architecture](https://mlsanity.com/paper/2505.09907/) (2025)

## Built on by (most cited first, 5 of 5)

- [Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA](https://mlsanity.com/paper/2512.12812/) (2025)
- [Do Clinical Question Answering Systems Really Need Specialised Medical Fine Tuning?](https://mlsanity.com/paper/2601.12812/) (2026)
- [To Reason or Not to: Selective Chain-of-Thought in Medical Question Answering](https://mlsanity.com/paper/2602.20130/) (2026)
- [Uncertainty Reasoning with Large Language Models for Explainable Disease Diagnosis](https://mlsanity.com/paper/2605.25566/) (2026)
- [Let LLMs Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering](https://mlsanity.com/paper/2606.15419/) (2026)

## Abstract (arXiv)

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.