Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning

arXiv:2608.26166v1 Announce Type: cross Abstract: Advancing reasoning capabilities allow large language models (LLMs) to tackle increasingly complex problems, while reasoning traces - intermediate steps toward solutions - open up high-stakes applications by enabling human inspection of AI decision-making. However, current approaches prioritize model performance over human interpretability, limiting effective human-AI collaboration. In this study, we design and evaluate a human-centered approach that structures reasoning traces based on self-contained, verifiable steps, enabling users to independently assess and correct AI reasoning. Our approach uses XML-like tags to encode reasoning content and metadata, facilitating targeted feedback. Evaluation on mathematical reasoning tasks shows our approach maintains equivalent performance to standard Chain-of-Thought reasoning while enhancing interpretability. User studies demonstrate significant improvements in perceived usefulness and ease of use. This work advances understanding of how user-centric design of LLM outputs can better serve human collaboration needs in high-stakes AI deployments.

Abstract:Advancing reasoning capabilities allow large language models (LLMs) to tackle increasingly complex problems, while reasoning traces - intermediate steps toward solutions - open up high-stakes applications by enabling human inspection of AI decision-making. However, current approaches prioritize model performance over human interpretability, limiting effective human-AI collaboration. In this study, we design and evaluate a human-centered approach that structures reasoning traces based on self-contained, verifiable steps, enabling users to independently assess and correct AI reasoning. Our approach uses XML-like tags to encode reasoning content and metadata, facilitating targeted feedback. Evaluation on mathematical reasoning tasks shows our approach maintains equivalent performance to standard Chain-of-Thought reasoning while enhancing interpretability. User studies demonstrate significant improvements in perceived usefulness and ease of use. This work advances understanding of how user-centric design of LLM outputs can better serve human collaboration needs in high-stakes AI deployments.

Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)

arXiv:2608.26166 [cs.HC]

(or arXiv:2608.26166v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2608.26166 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proceedings of the 59th Hawaii International Conference on System Sciences (pp. 1435-1444). University of Hawai'i at Manoa, 2026

https://doi.org/10.24251/HICSS.2026.171 Focus to learn more DOI(s) linking to related resources

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Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning

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