arXiv:2602.11201v2 Announce Type: replace Abstract: Chain-of-Thought (CoT) explanations are widely used to interpret how language models solve complex problems, yet it remains unclear whether these step-by-step explanations reflect how the model actually reaches its answer, or merely post-hoc justifications. We propose Normalized Logit Difference Decay (NLDD), a metric that measures whether individual reasoning steps are faithful to the model's decision-making process. Our approach corrupts individual reasoning steps from the explanation and measures how much the model's confidence in its answ
The rapid deployment and increasing reliance on large language models for complex problem-solving necessitates robust interpretability methods to ensure reliability and trustworthiness.
Understanding the faithfulness of Chain-of-Thought reasoning is crucial for validating AI decision-making processes, especially as these models are deployed in high-stakes environments.
This research introduces a novel metric that allows for a more rigorous and quantitative assessment of how accurately AI explanations reflect the model's actual reasoning, shifting interpretability from qualitative to more evidence-based assessment.
- · AI interpretability researchers
- · Developers of robust AI systems
- · Sectors requiring high AI trustworthiness
- · AI models with unfaithful explanations
- · Users relying solely on CoT for interpretability
Increased scrutiny and demand for 'explainable AI' (XAI) tools that can verify reasoning fidelity.
Development of new training techniques for LLMs specifically designed to produce more faithful and transparent reasoning paths.
Potential regulatory frameworks requiring certified faithfulness metrics for AI systems used in critical applications.
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