Identifying task groupings for multi-task learning using pointwise V-usable information

doi:10.1016/j.jbi.2025.104881

2025-07-16

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DOI：10.1016/j.jbi.2025.104881

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摘要

En 中文

即使在声称能解决许多任务的Large Language Models (LLMs)时代，微调语言模型仍然是部署中采用的一种核心方法，原因有多种——其中包括计算效率与性能最大化。微调可以是单任务或多任务联合学习，其中任务之间相互支持，从而提升性能。多任务学习的成功可能高度依赖于哪些任务被组合在一起。简单地组合所有任务或一组随机任务可能导致负迁移，使多任务模型的性能劣于单任务模型。尽管已有很多工作致力于识别任务分组及衡量不同任务间的相关性，但如何定义一个指标来从众多潜在的任务组合中识别出最佳任务分组，仍然是一个具有挑战性的研究课题。我们提出了这样一个指标。

期刊

Journal of Biomedical Informatics

IF:

4.5

论文数:

3.5K

被引数:

1.9W

机构

the university of arizona

学者数:

288

论文数: 147

被引数: 0

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