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

• URL: http://arxiv.org/abs/2408.07888v2
• Date: Tue, 5 Nov 2024 11:07:19 GMT
• Title: Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering
• Authors: Yushi Yang, Andrew M. Bean, Robert McCraith, Adam Mahdi,
• Abstract summary: 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. strategies achieve the best accuracy gain of 1.81% and an average gain of 1.02% across datasets.
• Score: 1.912429179274357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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