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Decoupling the Effect of Chain-of-Thought Reasoning: A Human Label Variation Perspective

MCML Authors

Beiduo Chen

→ Group Barbara Plank
AI and Computational Linguistics

Robert Litschko

→ Group Barbara Plank
AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Core PI

AI and Computational Linguistics

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.

inproceedings CHZ+26

Findings @ACL 2026

Findings at the 64th Annual Meeting of the Association for Computational Linguistics. San Diego, CA, USA, Jul 02-07, 2026. To be published. Preprint available.