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Fine-tuning Large Language Models with Human-Inspired Learning Strategies in Medical Question Answering

Large Language Models (LLMs) are powerful tools for a variety of tasks, but training them is expensive. One way to reduce the cost of training LLMs is to use human-inspired learning strategies, which are based on how humans learn. In a new paper, researchers at the University of Oxford explore the effectiveness of five human-inspired learning strategies for fine-tuning LLMs for medical question answering. The researchers fine-tune four LLMs on the Lekarski Egzamin Końcowy (LEK) dataset, a Polish medical licensing exam dataset, and evaluate their performance on three test datasets.

The researchers find that the effectiveness of human-inspired learning strategies varies significantly across different models and datasets. For example, Curriculum Learning, which orders training examples from easiest to hardest, results in the highest accuracy gains for some models and datasets but not for others.

The research team also investigate whether using LLM-defined question difficulty is as effective as using human-defined question difficulty. They find that using LLM-defined question difficulty does lead to modest accuracy improvements for curriculum-based learning strategies, suggesting that LLMs can be used to generate effective learning curricula. This is an important finding because it suggests that LLMs can be used to reduce the cost of developing learning curricula.

The researchers’ findings highlight the potential of using human-inspired learning strategies to improve the efficiency of training LLMs for medical question answering. However, they also emphasize that the effectiveness of these strategies varies significantly across different models and datasets, so careful evaluation is needed to determine the best strategy for a given task.

The researchers’ work has implications for the development of more efficient and effective LLMs for medical question answering. By understanding the factors that influence the effectiveness of human-inspired learning strategies, researchers can develop better methods for fine-tuning LLMs and improve their performance on a variety of tasks.