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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2408.07888 |
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| _version_ | 1866929575997472768 |
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| author | Yang, Yushi Bean, Andrew M. McCraith, Robert Mahdi, Adam |
| author_facet | Yang, Yushi Bean, Andrew M. McCraith, Robert Mahdi, Adam |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07888 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering Yang, Yushi Bean, Andrew M. McCraith, Robert Mahdi, Adam Computation and Language 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. |
| title | Evaluating Fine-Tuning Efficiency of Human-Inspired Learning Strategies in Medical Question Answering |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2408.07888 |