使用点v可用信息识别多任务学习的任务分组。

IF 4.5 2区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Journal of Biomedical Informatics Pub Date : 2025-09-01 Epub Date: 2025-07-16 DOI:10.1016/j.jbi.2025.104881
Yingya Li, Timothy Miller, Steven Bethard, Guergana Savova
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引用次数: 0

摘要

目的:即使在大型语言模型(llm)时代,它被认为是许多任务的解决方案,微调语言模型仍然是部署中使用的核心方法,原因有很多,其中包括计算效率和性能最大化。微调可以是单任务或多任务联合学习,其中任务相互支持从而提高它们的表现。多任务学习的成功在很大程度上取决于哪些任务被组合在一起。天真地将所有任务或一组随机任务分组可能会导致负迁移,多任务模型的表现比单任务模型更差。尽管在识别任务分组和测量不同任务之间的相关性方面已经做出了许多努力,但定义一个度量来从许多潜在的任务组合池中识别最佳任务分组仍然是一个具有挑战性的研究课题。我们提出这样一个度量标准。方法:提出了一种基于点向v可用信息(PVI)测量任务难度的任务相关性度量方法。PVI是最近提出的一个度量,用于估计给定模型的数据集包含多少可用信息。我们假设PVI估计没有统计学差异的任务足够相似,可以从联合学习过程中受益。我们进行了全面的实验,以评估该指标在一般、生物医学和临床领域的15个NLP数据集上进行任务分组的可行性。我们将联合学习器的结果与单个学习器、现有的基线方法和最近的大型语言模型(包括Llama和GPT-4)进行了比较。结果:结果表明,通过对具有相似PVI估计的任务进行分组,联合学习器在总参数较少的情况下产生了具有竞争力的结果,并且在各个领域的表现一致。结论:对于特定领域的任务,微调模型可能仍然是一个更好的选择,基于pvi的多任务学习任务分组方法可能特别有益。这个度量可以包含在微调语言模型的整体配方中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying task groupings for multi-task learning using pointwise V-usable information.

Objective: Even in the era of Large Language Models (LLMs) which are claimed to be solutions for many tasks, fine-tuning language models remains a core methodology used in deployment for a variety of reasons - computational efficiency and performance maximization among them. Fine-tuning could be single-task or multi-task joint learning where the tasks support each other thus boosting their performance. The success of multi-task learning can depend heavily on which tasks are grouped together. Naively grouping all tasks or a random set of tasks can result in negative transfer, with the multi-task models performing worse than single-task models. Though many efforts have been made to identify task groupings and to measure the relatedness among different tasks, it remains a challenging research topic to define a metric to identify the best task grouping out of a pool of many potential task combinations. We propose such a metric.

Methods: We propose a metric of task relatedness based on task difficulty measured by pointwise V-usable information (PVI). PVI is a recently proposed metric to estimate how much usable information a dataset contains given a model. We hypothesize that tasks with not statistically different PVI estimates are similar enough to benefit from the joint learning process. We conduct comprehensive experiments to evaluate the feasibility of this metric for task grouping on 15 NLP datasets in the general, biomedical, and clinical domains. We compare the results of the joint learners against single learners, existing baseline methods, and recent large language models, including Llama and GPT-4.

Results: The results show that by grouping tasks with similar PVI estimates, the joint learners yielded competitive results with fewer total parameters, with consistent performance across domains.

Conclusion: For domain-specific tasks, finetuned models may remain a preferable option, and the PVI-based method of grouping tasks for multi-task learning could be particularly beneficial. This metric could be wrapped in the overall recipe of fine-tuning language models.

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来源期刊
Journal of Biomedical Informatics
Journal of Biomedical Informatics 医学-计算机:跨学科应用
CiteScore
8.90
自引率
6.70%
发文量
243
审稿时长
32 days
期刊介绍: The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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