Fine-tuning Large Language Models with Human-inspired Learning Strategies in Medical Question Answering
<|reference_start|>Fine-tuning Large Language Models with Human-inspired Learning Strategies in Medical Question Answering: Training Large Language Models (LLMs) incurs substantial data-related costs, motivating the development of data-efficient training methods through optimised data ordering and selection. Human-inspired learning strategies, such as curriculum learning, offer possibilities for efficient training by organising data according to common human learning practices. Despite evidence that fine-tuning with curriculum learning improves the performance of LLMs for natural language understanding tasks, its effectiveness is typically assessed using a single model. In this work, we extend previous research by evaluating both curriculum-based and non-curriculum-based learning strategies across multiple LLMs, using human-defined and automated data labels for medical question answering. Our results indicate a moderate impact of using human-inspired learning strategies for fine-tuning LLMs, with maximum accuracy gains of 1.77% per model and 1.81% per dataset. Crucially, we demonstrate that the effectiveness of these strategies varies significantly across different model-dataset combinations, emphasising that the benefits of a specific human-inspired strategy for fine-tuning LLMs do not generalise. Additionally, we find evidence that curriculum learning using LLM-defined question difficulty outperforms human-defined difficulty, highlighting the potential of using model-generated measures for optimal curriculum design.<|reference_end|>
@article{yang2024evaluating,
    title={Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies
  in Medical Question Answering},
    author={Yushi Yang, Andrew M. Bean, Robert McCraith, Adam Mahdi},
    journal={arXiv preprint arXiv:2408.07888},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07888},
    primaryClass={cs.CL}
}