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        <title>Improving Multitask Retrieval by Promoting Task Specialization</title>
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        <namePart type="given">Wenzheng</namePart>
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    <abstract>In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval, in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outperforms task-specific retrievers by promoting task specialization. The main ingredients are: (1) a better choice of pretrained model—one that is explicitly optimized for multitasking—along with compatible prompting, and (2) a novel adaptive learning method that encourages each parameter to specialize in a particular task. The resulting multitask retriever is highly performant on the KILT benchmark. Upon analysis, we find that the model indeed learns parameters that are more task-specialized compared to naive multitasking without prompting or adaptive learning.1</abstract>
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%0 Journal Article
%T Improving Multitask Retrieval by Promoting Task Specialization
%A Zhang, Wenzheng
%A Xiong, Chenyan
%A Stratos, Karl
%A Overwijk, Arnold
%J Transactions of the Association for Computational Linguistics
%D 2023
%V 11
%I MIT Press
%C Cambridge, MA
%F zhang-etal-2023-improving-multitask
%X In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval, in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outperforms task-specific retrievers by promoting task specialization. The main ingredients are: (1) a better choice of pretrained model—one that is explicitly optimized for multitasking—along with compatible prompting, and (2) a novel adaptive learning method that encourages each parameter to specialize in a particular task. The resulting multitask retriever is highly performant on the KILT benchmark. Upon analysis, we find that the model indeed learns parameters that are more task-specialized compared to naive multitasking without prompting or adaptive learning.1
%R 10.1162/tacl_a_00597
%U https://aclanthology.org/2023.tacl-1.68/
%U https://doi.org/10.1162/tacl_a_00597
%P 1201-1212

Markdown (Informal)

[Improving Multitask Retrieval by Promoting Task Specialization](https://aclanthology.org/2023.tacl-1.68/) (Zhang et al., TACL 2023)

• Improving Multitask Retrieval by Promoting Task Specialization (Zhang et al., TACL 2023)

ACL

• Wenzheng Zhang, Chenyan Xiong, Karl Stratos, and Arnold Overwijk. 2023. Improving Multitask Retrieval by Promoting Task Specialization. Transactions of the Association for Computational Linguistics, 11:1201–1212.