AI

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

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 07/10/2026, 02:18:24 UTC.

Briefings

A 2026 study introduces CoT-Interpretability Alignment (CIA) to measure agreement between LLMs' stated reasoning traces and their internal computations. Evaluated on three tasks across three LLMs, CIA scores ranged from 44.8% to 75.9%, indicating limited alignment. The research proposes post-training methods using task accuracy and parametric faithfulness as rewards to improve CIA while maintaining performance. Code and data are available for reproducibility.

Key points

• The study introduces CoT-Interpretability Alignment (CIA) as a metric to measure agreement between LLMs' stated reasoning traces and their internal computations.
• CIA scores ranged from 44.8% to 75.9% across three tasks and three LLMs, indicating limited alignment between stated reasoning and internal computations.

Context

Chain-of-thought traces serve as a proxy for LLM reasoning, but models often fail to reflect internal computations. This work measures and improves alignment between stated reasoning and internal computations using the CIA metric.

Limitations

Evidence is limited to three tasks and three LLMs; generalization patterns are analyzed but not fully quantified. Results are from a 2026 study with code and data available for reproducibility.

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