← Back to all news
arXiv Computation and Language By Yujing Wang, Yulan He, Wenya Wang, Hanqi Yan

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

Read the original on arXiv Computation and Language →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv AI
Oct 1

Making LLMs Say What They Think: Measuring and Improving CoT-Interpretability Alignment

The paper introduces CoT-Interpretability Alignment (CIA), a metric that quantifies how well a large language model’s chain-of-thought (CoT) explanations match its internal reasoning processes. Evaluated on two-hop question answering, hint intervention, and integer multiplication across three LLMs, the study finds limited alignment (44.8–75.9%) and demonstrates that post‑training with a reward combining task accuracy and parametric faithfulness can substantially improve CoT faithfulness without sacrificing accuracy. The authors provide a framework for auditing CoT faithfulness and a pathway to making explicit reasoning more trustworthy, with code and data publicly available.

By Yihuai Hong, Shauli Ravfogel, Chen Zhao, Eunsol Choi
llmsnlpsafety
More like this →