@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://aclanthology.org/2025.acl-long.24/",
doi = "10.18653/v1/2025.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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gao-etal-2025-uniicl">
<titleInfo>
<title>UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Gao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Qi</namePart>
<namePart type="family">Lv</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Zili</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tianxiang</namePart>
<namePart type="family">Wu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ziqiang</namePart>
<namePart type="family">Cao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Wenjie</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Wanxiang</namePart>
<namePart type="family">Che</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Joyce</namePart>
<namePart type="family">Nabende</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ekaterina</namePart>
<namePart type="family">Shutova</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mohammad</namePart>
<namePart type="given">Taher</namePart>
<namePart type="family">Pilehvar</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Vienna, Austria</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-251-0</identifier>
</relatedItem>
<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 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.</abstract>
<identifier type="citekey">gao-etal-2025-uniicl</identifier>
<identifier type="doi">10.18653/v1/2025.acl-long.24</identifier>
<location>
<url>https://aclanthology.org/2025.acl-long.24/</url>
</location>
<part>
<date>2025-07</date>
<extent unit="page">
<start>500</start>
<end>510</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation
%A Gao, Jun
%A Lv, Qi
%A Wang, Zili
%A Wu, Tianxiang
%A Cao, Ziqiang
%A Li, Wenjie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F gao-etal-2025-uniicl
%X 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 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.
%R 10.18653/v1/2025.acl-long.24
%U https://aclanthology.org/2025.acl-long.24/
%U https://doi.org/10.18653/v1/2025.acl-long.24
%P 500-510
Markdown (Informal)
[UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation](https://aclanthology.org/2025.acl-long.24/) (Gao et al., ACL 2025)
ACL
- Jun Gao, Qi Lv, Zili Wang, Tianxiang Wu, Ziqiang Cao, and Wenjie Li. 2025. UniICL: An Efficient ICL Framework Unifying Compression, Selection, and Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 500–510, Vienna, Austria. Association for Computational Linguistics.