@inproceedings{zhang-etal-2026-c3d,
    title = "{C}$^3${D}: Enhancing {LLM} Reasoning via Confidence-Guided Contrastive Decoding",
    author = "Zhang, Yufeng  and
      Wang, Xuepeng  and
      Wu, Lingxiang  and
      Wang, Jinqiao",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.33/",
    pages = "700--712",
    ISBN = "979-8-89176-395-1",
    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."
}