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arXiv · 2405.11465

Effective In-Context Example Selection through Data Compression

Zhongxiang Sun·Kepu Zhang·Haoyu Wang·Xiao Zhang·Jun Xu
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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.

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Zhongxiang Sun, Kepu Zhang, Haoyu Wang, Xiao Zhang, Jun Xu. 2024-05-19. Effective In-Context Example Selection through Data Compression. https://arxiv.org/abs/2405.11465

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