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        <title>Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding</title>
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    <abstract>Large language models (LLMs) excel at a range of tasks through in-context learning (ICL), where only a few task examples guide their predictions. However, prior research highlights that LLMs often overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. To address this issue, we introduce In-Context Contrastive Decoding (ICCD), a novel method that emphasizes input-label mapping by contrasting the output distributions between positive and negative in-context examples. Experiments on 7 natural language understanding (NLU) tasks show that our ICCD method brings consistent and significant improvement (up to +1.8 improvement on average) upon 6 different scales of LLMs without requiring additional training. Our approach is versatile, enhancing performance with various demonstration selection methods, demonstrating its broad applicability and effectiveness. The code and scripts are released at https://github.com/Romainpkq/CD_ICL.</abstract>
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%0 Conference Proceedings
%T Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding
%A Peng, Keqin
%A Ding, Liang
%A Ouyang, Yuanxin
%A Fang, Meng
%A Yuan, Yancheng
%A Tao, Dacheng
%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 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F peng-etal-2025-enhancing
%X Large language models (LLMs) excel at a range of tasks through in-context learning (ICL), where only a few task examples guide their predictions. However, prior research highlights that LLMs often overlook input-label mapping information in ICL, relying more on their pre-trained knowledge. To address this issue, we introduce In-Context Contrastive Decoding (ICCD), a novel method that emphasizes input-label mapping by contrasting the output distributions between positive and negative in-context examples. Experiments on 7 natural language understanding (NLU) tasks show that our ICCD method brings consistent and significant improvement (up to +1.8 improvement on average) upon 6 different scales of LLMs without requiring additional training. Our approach is versatile, enhancing performance with various demonstration selection methods, demonstrating its broad applicability and effectiveness. The code and scripts are released at https://github.com/Romainpkq/CD_ICL.
%R 10.18653/v1/2025.acl-short.77
%U https://aclanthology.org/2025.acl-short.77/
%U https://doi.org/10.18653/v1/2025.acl-short.77
%P 997-1004

Markdown (Informal)

[Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding](https://aclanthology.org/2025.acl-short.77/) (Peng et al., ACL 2025)

• Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding (Peng et al., ACL 2025)

ACL

• Keqin Peng, Liang Ding, Yuanxin Ouyang, Meng Fang, Yancheng Yuan, and Dacheng Tao. 2025. Enhancing Input-Label Mapping in In-Context Learning with Contrastive Decoding. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 997–1004, Vienna, Austria. Association for Computational Linguistics.