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March 26, 2026Applied SciencesOpen Access

Testing Explainability of Chain of Thought for Large Language Models

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Authors

HCHao ChenZZZhe ZhaoZSZiqi Shuai

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Overview

Automated testing reveals insufficient and unnecessary reasoning in large language models' chains of thought, implying limitations in explanation.

Key Points

  • The aim is to evaluate the explainability of Chains of Thought in large language models, focusing on their reasoning capabilities.
  • Proposed an automated approach to test responses to self-cited evidence in Chains of Thought under context intervention.
  • Intervened in reasoning chains by altering input context.
  • Measured behavioral consistency as a proxy for CoT faithfulness.
  • Conducted tests on mainstream open-source large language models using multi-hop question-answering tasks.
  • Experimental results indicate that Chains of Thought are insufficient for complete explanation.
  • Findings show that the reasoning provided by CoTs is unnecessary in some contexts.
  • The ability of CoTs to explain model behavior is limited.

Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/69c4cd3efdc3bde44891946ahttps://doi.org/10.3390/app16073112
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