What Makes Chain-of-Thought Work at Probe Time? Local Co-occurrence Rather Than Global Derivation
arXiv:2605.26795v1 Announce Type: new Abstract: Chain-of-thought (CoT) prompting reliably improves language-model accuracy, but which properties of a rationale text drive the improvement is poorly understood. Prior work has largely studied generation-time behavior. We instead ask a probe-time question: given a fixed rationale in context, what in that text changes the answer? We identify two complementary sources of the gain. First, even a globally word-shuffled rationale substantially outperforms the no-rationale baseline, indicating a strong lexical activation effect. More importantly, the ad
This research provides a deeper, albeit technical, understanding of how Chain-of-Thought prompting functions, moving beyond anecdotal observations to mechanistic explanations.
Understanding the precise mechanisms of CoT will enable more efficient and robust prompt engineering, accelerating the development of advanced AI applications and agents.
The focus shifts from merely observing CoT efficacy to dissecting its underlying cognitive-like processes, suggesting new avenues for designing more effective prompts.
- · AI researchers
- · Prompt engineers
- · AI developers
- · Inefficient prompt engineering methodologies
More targeted and effective prompt designs will improve the performance and reliability of language models.
This improved understanding could lead to new architectural insights for language models, making them inherently more 'reasoning' capable.
Enhanced LLM capabilities could accelerate the development and reliability of AI agents, facilitating their integration into complex workflows.
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Read at arXiv cs.AI