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.
66c015458c3816c56301187c
Yushi Yang
claimed_verified
2024-08-17T05:38:53.006Z
6562737bf5532ac1bd09d3bd
/avatars/aaaff49b6abb5f9351159006b7755d25.svg
YY
user
yy0514
66c015458c3816c56301187d
Andrew M. Bean
66c015458c3816c56301187e
Robert McCraith
66c015458c3816c56301187f
Adam Mahdi
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.
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.
curriculum learning fine-tuning large language models model-generated measures medical question answering human-defined difficulty model-defined question difficulty