@inproceedings{sun-etal-2024-effective,
    title = "Effective In-Context Example Selection through Data Compression",
    author = "Sun, ZhongXiang  and
      Zhang, Kepu  and
      Wang, Haoyu  and
      Zhang, Xiao  and
      Xu, Jun",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2024.findings-acl.50/",
    doi = "10.18653/v1/2024.findings-acl.50",
    pages = "871--877",
    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."
}