arXiv AI By Leonardo Matthew Yauw, Wei-Bin Kou, Yujiu Yang

Integrated and Cross-Architecture Interpretation of LLM Reasoning

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arXiv Computation and Language
Oct 19

Verbalising Model's Computation for Chain-of-Thought Faithfulness Detection

The paper introduces VERITAS, a method that verbalises a language model’s internal computation during chain‑of‑thought (CoT) generation by projecting layer‑wise Jacobians to produce a token‑by‑token account of reasoning evolution. By training a classifier on these verbalised features, VERITAS detects where and how reasoning diverges toward unfaithfulness, achieving a 28.4% balanced‑accuracy gain over LLM‑as‑Judge and a 16.0% improvement over activation probing across four datasets. The approach demonstrates generalisability to different datasets and model architectures, offering a richer, more interpretable lens on CoT faithfulness that moves beyond detection toward mechanistic insight.

By Yujing Wang, Yulan He, Wenya Wang, Hanqi Yan