title: Fine-tuning Large Language Models with Human-inspired Learning
    Strategies in Medical Question Answering
summary: 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.
paper_authors._id: 66c015458c3816c56301187c
paper_authors.name: Yushi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-17T05:38:53.006Z
paper_authors._id: 6562737bf5532ac1bd09d3bd
paper_authors.avatarUrl: /avatars/aaaff49b6abb5f9351159006b7755d25.svg
paper_authors.fullname: YY
paper_authors.type: user
paper_authors.user: yy0514
paper_authors._id: 66c015458c3816c56301187d
paper_authors.name: Andrew M. Bean
paper_authors._id: 66c015458c3816c56301187e
paper_authors.name: Robert McCraith
paper_authors._id: 66c015458c3816c56301187f
paper_authors.name: Adam Mahdi
paper_title: Fine-tuning Large Language Models with Human-inspired Learning
    Strategies in Medical Question Answering
paper_summary: 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.
paper_ai_summary: Evaluating curriculum-based and non-curriculum-based learning strategies across multiple large language models for medical question answering shows variable effectiveness, with model-generated question difficulty measures potentially outperforming human-defined ones.
paper_ai_keywords: curriculum learning fine-tuning large language models model-generated measures medical question answering human-defined difficulty model-defined question difficulty