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🌀 Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
This research paper examines how chain-of-thought (CoT) prompting—encouraging models to reason step-by-step—affects large language and multimodal model performance across tasks. While CoT generally boosts performance, the authors find it significantly hampers model accuracy in three specific contexts: implicit statistical learning, facial recognition, and classifying data with exceptions. The paper suggests a similarity between CoT and human verbal reasoning, proposing that tasks where deliberate thinking harms human performance may similarly impair models using CoT. The study concludes that recognizing scenarios where reasoning is counterproductive for humans can highlight situations where CoT also hinders model effectiveness.
📎 Link to paper
This research paper examines how chain-of-thought (CoT) prompting—encouraging models to reason step-by-step—affects large language and multimodal model performance across tasks. While CoT generally boosts performance, the authors find it significantly hampers model accuracy in three specific contexts: implicit statistical learning, facial recognition, and classifying data with exceptions. The paper suggests a similarity between CoT and human verbal reasoning, proposing that tasks where deliberate thinking harms human performance may similarly impair models using CoT. The study concludes that recognizing scenarios where reasoning is counterproductive for humans can highlight situations where CoT also hinders model effectiveness.
📎 Link to paper
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