@inproceedings{sun-etal-2025-interpret,
title = "Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings",
author = "Sun, Chenghao and
Huang, Zhen and
Zhang, Yonggang and
Lu, Le and
Li, Houqiang and
Tian, Xinmei and
Shen, Xu and
Ye, Jieping",
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.196/",
doi = "10.18653/v1/2025.acl-long.196",
pages = "3873--3895",
ISBN = "979-8-89176-251-0",
abstract = "Large language models (LLMs) excel at downstream NLP tasks through in-context learning (ICL) with a few demonstrations of input{--}label pairs. However, the internal mechanisms behind ICL remain under-explored, particularly the mappings between inputs and labels. In this work, we reverse-engineer ICL by examining input-label mappings: what they are within LLMs, where they function, and how LLMs utilize them. (1) what: We discover input-label mappings stored within a few specific layers in the form of principal components (PCs), which capture human-interpretable and task-related words. (2) where: We propose a PC patching approach to identify the modules where input-label mappings function. Specifically, PC patching automatically crafts counterfactual representations using identified semantic PCs, rather than manually designing counterfactual text, to suppress the behavior related to LLM capability for ICL-related modules. Utilizing PC patching, we identify LLMs apply input-label mappings in a small fraction of attention heads. (3) how: We observe and verify that the identified key heads utilize input-label mappings from demonstrations to generate target labels for new queries. Based on these discoveries, we further show that precisely fine-tuning key ICL-related modules leads to significant improvements across diverse tasks."
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<abstract>Large language models (LLMs) excel at downstream NLP tasks through in-context learning (ICL) with a few demonstrations of input–label pairs. However, the internal mechanisms behind ICL remain under-explored, particularly the mappings between inputs and labels. In this work, we reverse-engineer ICL by examining input-label mappings: what they are within LLMs, where they function, and how LLMs utilize them. (1) what: We discover input-label mappings stored within a few specific layers in the form of principal components (PCs), which capture human-interpretable and task-related words. (2) where: We propose a PC patching approach to identify the modules where input-label mappings function. Specifically, PC patching automatically crafts counterfactual representations using identified semantic PCs, rather than manually designing counterfactual text, to suppress the behavior related to LLM capability for ICL-related modules. Utilizing PC patching, we identify LLMs apply input-label mappings in a small fraction of attention heads. (3) how: We observe and verify that the identified key heads utilize input-label mappings from demonstrations to generate target labels for new queries. Based on these discoveries, we further show that precisely fine-tuning key ICL-related modules leads to significant improvements across diverse tasks.</abstract>
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%0 Conference Proceedings
%T Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings
%A Sun, Chenghao
%A Huang, Zhen
%A Zhang, Yonggang
%A Lu, Le
%A Li, Houqiang
%A Tian, Xinmei
%A Shen, Xu
%A Ye, Jieping
%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 sun-etal-2025-interpret
%X Large language models (LLMs) excel at downstream NLP tasks through in-context learning (ICL) with a few demonstrations of input–label pairs. However, the internal mechanisms behind ICL remain under-explored, particularly the mappings between inputs and labels. In this work, we reverse-engineer ICL by examining input-label mappings: what they are within LLMs, where they function, and how LLMs utilize them. (1) what: We discover input-label mappings stored within a few specific layers in the form of principal components (PCs), which capture human-interpretable and task-related words. (2) where: We propose a PC patching approach to identify the modules where input-label mappings function. Specifically, PC patching automatically crafts counterfactual representations using identified semantic PCs, rather than manually designing counterfactual text, to suppress the behavior related to LLM capability for ICL-related modules. Utilizing PC patching, we identify LLMs apply input-label mappings in a small fraction of attention heads. (3) how: We observe and verify that the identified key heads utilize input-label mappings from demonstrations to generate target labels for new queries. Based on these discoveries, we further show that precisely fine-tuning key ICL-related modules leads to significant improvements across diverse tasks.
%R 10.18653/v1/2025.acl-long.196
%U https://aclanthology.org/2025.acl-long.196/
%U https://doi.org/10.18653/v1/2025.acl-long.196
%P 3873-3895
Markdown (Informal)
[Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings](https://aclanthology.org/2025.acl-long.196/) (Sun et al., ACL 2025)
ACL
- Chenghao Sun, Zhen Huang, Yonggang Zhang, Le Lu, Houqiang Li, Xinmei Tian, Xu Shen, and Jieping Ye. 2025. Interpret and Improve In-Context Learning via the Lens of Input-Label Mappings. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3873–3895, Vienna, Austria. Association for Computational Linguistics.