The paper finds that as exemplar count increases, LLMs shift from pretrained priors to in-context signals, and long chain-of-thought prompts induce longer reasoning chains that often improve accuracy.
In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Rethinking the Chain-of-Thought: The Roles of In-Context Learning and Pre-trained Priors
The paper finds that as exemplar count increases, LLMs shift from pretrained priors to in-context signals, and long chain-of-thought prompts induce longer reasoning chains that often improve accuracy.