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        <title>C³D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding</title>
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        <namePart type="given">Yufeng</namePart>
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    <abstract>Large language models (LLMs) are prone to distraction by contextual information during reasoning. Previous work primarily focuses on improving the generation of the next token while overlooking the potential bias introduced by existing premises. We propose a novel decoding method to mitigate such biases. Our framework uses predicted logits to estimate the model’s confidence. By decomposing the full context into multiple premises, we gain a clearer understanding of the relevance of each premise to the question. During next-token prediction, we refine the output by contrasting the logits with the highest and lowest confidence. Our method effectively reveals how the model dynamically activates and adjusts its consideration of each premise as reasoning progresses.</abstract>
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%0 Conference Proceedings
%T C³D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding
%A Zhang, Yufeng
%A Wang, Xuepeng
%A Wu, Lingxiang
%A Wang, Jinqiao
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhang-etal-2026-c3d
%X Large language models (LLMs) are prone to distraction by contextual information during reasoning. Previous work primarily focuses on improving the generation of the next token while overlooking the potential bias introduced by existing premises. We propose a novel decoding method to mitigate such biases. Our framework uses predicted logits to estimate the model’s confidence. By decomposing the full context into multiple premises, we gain a clearer understanding of the relevance of each premise to the question. During next-token prediction, we refine the output by contrasting the logits with the highest and lowest confidence. Our method effectively reveals how the model dynamically activates and adjusts its consideration of each premise as reasoning progresses.
%U https://aclanthology.org/2026.findings-acl.33/
%P 700-712

Markdown (Informal)

[C3D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding](https://aclanthology.org/2026.findings-acl.33/) (Zhang et al., Findings 2026)

• C3D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding (Zhang et al., Findings 2026)

ACL

• Yufeng Zhang, Xuepeng Wang, Lingxiang Wu, and Jinqiao Wang. 2026. C3D: Enhancing LLM Reasoning via Confidence-Guided Contrastive Decoding. In Findings of the Association for Computational Linguistics: ACL 2026, pages 700–712, San Diego, California, United States. Association for Computational Linguistics.