TaskWeb: Selecting Better Source Tasks for Multi-task NLP

• URL: http://arxiv.org/abs/2305.13256v2
• Date: Mon, 4 Dec 2023 04:35:55 GMT
• Title: TaskWeb: Selecting Better Source Tasks for Multi-task NLP
• Authors: Joongwon Kim, Akari Asai, Gabriel Ilharco, Hannaneh Hajishirzi
• Abstract summary: Knowing task relationships via pairwise task transfer improves choosing one or more source tasks that help to learn a new target task. We use TaskWeb to estimate the benefit of using a source task for learning a new target task, and to choose a subset of helpful training tasks for multi-task training. Our method improves overall rankings and top-k precision of source tasks by 10% and 38%, respectively.
• Score: 76.03221609799931
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work in NLP has shown promising results in training models on large amounts of tasks to achieve better generalization. However, it is not well-understood how tasks are related, and how helpful training tasks can be chosen for a new task. In this work, we investigate whether knowing task relationships via pairwise task transfer improves choosing one or more source tasks that help to learn a new target task. We provide TaskWeb, a large-scale benchmark of pairwise task transfers for 22 NLP tasks using three different model types, sizes, and adaptation methods, spanning about 25,000 experiments. Then, we design a new method TaskShop based on our analysis of TaskWeb. TaskShop uses TaskWeb to estimate the benefit of using a source task for learning a new target task, and to choose a subset of helpful training tasks for multi-task training. Our method improves overall rankings and top-k precision of source tasks by 10% and 38%, respectively. We also use TaskShop to build much smaller multi-task training sets that improve zero-shot performances across 11 different target tasks by at least 4.3%.

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