@inproceedings{chen-etal-2026-decoupling,
    title = "Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective",
    author = "Chen, Beiduo  and
      Hu, Tiancheng  and
      Zhang, Caiqi  and
      Litschko, Robert  and
      Korhonen, Anna  and
      Plank, Barbara",
    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.1103/",
    pages = "21931--21951",
    ISBN = "979-8-89176-395-1",
    abstract = "Reasoning-tuned LLMs utilizing long Chain-of-Thought (CoT) excel at single-answer tasks, yet their ability to model Human Label Variation{---}which requires capturing probabilistic ambiguity rather than resolving it{---}remains underexplored. We investigate this through systematic disentanglement experiments on distribution-based tasks, employing Cross-CoT experiments to isolate the effect of reasoning text from intrinsic model priors. We observe a distinct ``decoupled mechanism'': while CoT improves distributional alignment, final accuracy is dictated by CoT content (99{\%} variance contribution), whereas distributional ranking is governed by model priors (over 80{\%}). Step-wise analysis further shows that while CoT{'}s influence on accuracy grows monotonically during the reasoning process, distributional structure is largely determined by LLM{'}s intrinsic priors. These findings suggest that long CoT serves as a decisive LLM decision-maker for the top option but fails to function as a granular distribution calibrator for ambiguous tasks."
}