@inproceedings{gao-etal-2025-uniicl,
    title = "{U}ni{ICL}: An Efficient {ICL} Framework Unifying Compression, Selection, and Generation",
    author = "Gao, Jun  and
      Lv, Qi  and
      Wang, Zili  and
      Wu, Tianxiang  and
      Cao, Ziqiang  and
      Li, Wenjie",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.24/",
    pages = "500--510",
    ISBN = "979-8-89176-251-0",
    abstract = "In-context learning (ICL) enhances the reasoning abilities of Large Language Models (LLMs) by prepending a few demonstrations. It motivates researchers to introduce more examples to provide additional contextual information for the generation. However, existing methods show a significant limitation due to the problem of excessive growth in context length which causes a large hardware burden. Additionally, shallow-relevant examples selected by out-off-shelf tools hinder LLMs from capturing useful contextual information for generation. In this paper, to approach these limitations, we propose \textbf{UniICL}, a novel Unified ICL framework that unifies demonstration compression, demonstration selection, and final response generation. Furthermore, to avoid repeated compression of the same demonstration and boost inference efficiency, we design a tailored compression strategy that allows UniICL caching compression results into Demonstration Bank(DB). Extensive out-of-domain evaluations prove the advantages of UniICL in both effectiveness and efficiency."
}