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Resprompt: Residual Connection Prompting Advances Multi-Step Reasoning in Large Language Models
May 9, 2024, 4:47 a.m. | Song Jiang, Zahra Shakeri, Aaron Chan, Maziar Sanjabi, Hamed Firooz, Yinglong Xia, Bugra Akyildiz, Yizhou Sun, Jinchao Li, Qifan Wang, Asli Celikyilma
cs.CL updates on arXiv.org arxiv.org
Abstract: Chain-of-thought (CoT) prompting, which offers step-by-step problem-solving rationales, has impressively unlocked the reasoning potential of large language models (LLMs). Yet, the standard CoT is less effective in problems demanding multiple reasoning steps. This limitation arises from the complex reasoning process in multi-step problems: later stages often depend on the results of several steps earlier, not just the results of the immediately preceding step. Such complexities suggest the reasoning process is naturally represented as a graph. …
abstract advances arxiv cs.cl language language models large language large language models llms multiple problem-solving process prompting reasoning residual standard step-by-step thought type unlocked
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