Researchers have developed a new method called Correctness Conditioning to evaluate the faithfulness of Chain-of-Thought (CoT) reasoning in AI models. This technique focuses on instances where the model's final answer is correct, aiming to provide a more accurate measure of its reasoning process. By analyzing a seven-variable structural causal model, the study identifies and quantifies the gap introduced by this selection step, distinguishing it from other biases. Experiments on arithmetic tasks, including a run with GLM-5.3, showed positive selection gaps, indicating that restricting evaluation to correct answers can overestimate faithfulness. AI
IMPACT Introduces a refined method for evaluating AI reasoning, potentially leading to more reliable and trustworthy AI systems.
RANK_REASON Academic paper detailing a new methodology for evaluating AI model reasoning. [lever_c_demoted from research: ic=1 ai=1.0]
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