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        <title>Effective In-Context Example Selection through Data Compression</title>
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    <abstract>In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-stage method that can effectively choose relevant examples and retain sufficient information about the training dataset within the in-context examples. Our method shows a significant improvement of an average of 5.90% across five different real-world datasets using four language models.</abstract>
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%0 Conference Proceedings
%T Effective In-Context Example Selection through Data Compression
%A Sun, ZhongXiang
%A Zhang, Kepu
%A Wang, Haoyu
%A Zhang, Xiao
%A Xu, Jun
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sun-etal-2024-effective
%X In-context learning has been extensively validated in large language models. However, the mechanism and selection strategy for in-context example selection, which is a crucial ingredient in this approach, lacks systematic and in-depth research. In this paper, we propose a data compression approach to the selection of in-context examples. We introduce a two-stage method that can effectively choose relevant examples and retain sufficient information about the training dataset within the in-context examples. Our method shows a significant improvement of an average of 5.90% across five different real-world datasets using four language models.
%R 10.18653/v1/2024.findings-acl.50
%U https://aclanthology.org/2024.findings-acl.50
%U https://doi.org/10.18653/v1/2024.findings-acl.50
%P 871-877

Markdown (Informal)

[Effective In-Context Example Selection through Data Compression](https://aclanthology.org/2024.findings-acl.50) (Sun et al., Findings 2024)

• Effective In-Context Example Selection through Data Compression (Sun et al., Findings 2024)

ACL

• ZhongXiang Sun, Kepu Zhang, Haoyu Wang, Xiao Zhang, and Jun Xu. 2024. Effective In-Context Example Selection through Data Compression. In Findings of the Association for Computational Linguistics: ACL 2024, pages 871–877, Bangkok, Thailand. Association for Computational Linguistics.