Nov. 5, 2023, 6:47 a.m. | Jingyuan Qi, Zhiyang Xu, Ying Shen, Minqian Liu, Di Jin, Qifan Wang, Lifu Huang

cs.CL updates on arXiv.org arxiv.org

Chain-of-Thought (CoT) prompting enables large language models to solve
complex reasoning problems by generating intermediate steps. However, confined
by its inherent single-pass and sequential generation process, CoT heavily
relies on the initial decisions, causing errors in early steps to accumulate
and impact the final answers. In contrast, humans adopt recursive thinking when
tackling complex reasoning problems, i.e., iteratively breaking the original
problem into approachable sub-problems and aggregating their answers to resolve
the original one. Inspired by the human cognitive process, …

art arxiv decisions errors impact intermediate language language models large language large language models process prompting reasoning recursive solve thinking thought

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