Chain-of-thought faithfulness probing

How it works

Models often produce plausible step-by-step reasoning that does not reflect the computation behind the answer. By perturbing the reasoning trace, injecting biasing hints, or truncating steps and observing whether the final answer tracks the stated logic, you measure how much the explanation can be trusted — critical wherever the 'why' matters.

When to use it

High-stakes or regulated uses that rely on explanations; interpretability research.

Limitations

Faithfulness is hard to measure precisely; methods are still maturing.

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