Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization

Chain-of-Thought (CoT) faithfulness, i.e., whether CoTs genuinely reflect large language models' (LLM) underlying behavior, is typically evaluated under two disjoint paradigms: contextual faithfulness, measured by perturbing the input or CoT trace, and parametric faithfulness, assessed by intervening on a model's parametric knowledge. Yet prior work compares them only descriptively. We fill this gap by proposing FaithMate, a unified preference-alignment interface for optimizing models towards ei

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ethics.ai (24 May 2026), “Investigating the Interplay between Contextual and Parametric Chain-of-Thought Faithfulness under Optimization,” evidence record 3784, https://ethics.ai/record/3784 (originally published by arXiv).

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