Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering

Yushi Yang, Andrew Michael Bean, Robert McCraith, Adam Mahdi

Keywords: Large Language Models, Supervised Fine-tuning, Human-inspired learning, Medical Question Answering

TL;DR: This paper evaluates the efficiency of fine-tuning large language models using human-inspired learning strategies for medical question answering.

Abstract: Fine-tuning Large Language Models (LLMs) incurs considerable training costs, driving the need for data-efficient training with optimised data ordering. Human-inspired strategies offer a solution by organising data based on human learning practices. This study evaluates the fine-tuning efficiency of five human-inspired strategies across four language models, three datasets, and both human- and LLM-labelled data in the context of medical question answering. These strategies achieve the best accuracy gain of 1.81% and an average gain of 1.02% across datasets, with interleaved strategies delivering the best average results. However, the best strategy varies across model-dataset combinations, limiting the generalisability of the effects of any single strategy. Additionally, LLM-defined question difficulty outperforms human-defined labels in curriculum-based learning, showing the potential of model-generated data as a cost-effective alternative for optimising fine-tuning.

Submission Number: 95