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Back to the lesson libraryMECHANISM · 12 MIN
M21.1 CONNECT THE MECHANISM

Separate a correct solution from a convincing explanation

Ask a model to "think step by step" and it often gets more answers right. But is the reasoning it writes down how it really got there? You can test that.

LESSON OVERVIEW12 min lesson

Lesson overview

Ask a model to "think step by step" and it often gets more answers right. But is the reasoning it writes down how it really got there? You can test that.

What you’ll explore

  • Reasoning capability is evaluated through problem-solving behavior; generated explanations can help or mislead and are not guaranteed transcripts of the computation that caused an answer.
GO TO THE SOURCE

Original explanations, connected to the research.

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Wei et al., 2022)Large Language Models are Zero-Shot Reasoners (Kojima et al., 2022)Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting (Turpin et al., 2023)Measuring Faithfulness in Chain-of-Thought Reasoning (Lanham et al., 2023)
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