What question did this study set out to answer?

The aim is to explore whether measurable properties within Chain-of-Thought reasoning can provide reliable confidence signals in Large Language Models.

June 7, 2026Scientific ReportsOpen Access

Lexical hints of accuracy in LLM reasoning chains

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AVArne VanhoyweghenVrije Universiteit Brussel BVBrecht VerbekenVrije Universiteit Brussel AAAndres AlgabaVrije Universiteit Brussel

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Implication

Randomized trial finds lexical uncertainty cues improve accuracy prediction in language models, suggesting calibration potential.

Key Points

The aim is to explore whether measurable properties within Chain-of-Thought reasoning can provide reliable confidence signals in Large Language Models.
Analyzed three feature classes: CoT length, sentiment volatility, and lexicographic markers.
Evaluated these features using specific models on tasks including Humanity's Last Exam, Omni-MATH, and GPQA-diamond.
Applied the DeepSeek-R1 framework to assess the accuracy of the confidence signals derived from the CoT.
Lexical uncertainty cues were the most informative for predicting accuracy, while sentiment shifts were weaker signals.
CoT length was only predictive in intermediate difficulty tasks, showing no predictive power on harder tasks like HLE.
Uncertainty signals in the CoT were found to be more evident than high-confidence markers, making errors more detectable.

Cite This Study

Vanhoyweghen et al. (2026) studied this question.

synapsesocial.com/papers/6a250a717def13d035e1a92b https://doi.org/10.1038/s41598-026-55000-2

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Lexical hints of accuracy in LLM reasoning chains

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Member takes

Implication

Key Points

Cite This Study

Lexical hints of accuracy in LLM reasoning chains

Discussion

Member takes

Implication

Key Points

Cite This Study

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