总而言之,CoT推理能力只在大模型上能够成功,是一个复杂的现象,可能涉及到多种能力的涌现(semantic understanding, symbol mapping, staying on topic, arithmetic ability, faithfulness, etc)。 A.2. What is the role of prompt engineering? 我们的实验表明,CoT对prompt形式是具备鲁棒性的。不过,在个别的场景下...
Specifically, we explore the ability of language models to perform few-shot prompting for reasoning tasks, given a prompt that consists of triples: . A chain of thought is a series of intermediate natural language reasoning steps that lead to the final output, and we refer to this approach as...
Tree of Thoughts: Deliberate Problem Solving with Large Language Models(2023.05.17) Shunyu Yao, Dian Yu, Jeffrey Zhao, I. Shafran, T. Griffiths, etc . - 【arXiv.org】 Chain-of-Symbol Prompting Elicits Planning in Large Langauge Models(2023.05.17) ...
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Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks, arXiv.2211.12588 [paper] Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models, ACL 2023 [paper]
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models http://t.cn/A6iSa8yW 大模型确实在各个模型都有所提升的时候,会带来惊喜。 In summary, the success of chain-of-thought reasoning a...
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Note: When calling chains, you can use all of the same methods that a chat model supports. In general, the LCEL allows you to create arbitrary-length chains with the pipe symbol (|). For instance, if you wanted to format the model’s response, then you could add an output parser to...
Table 1: Accuracy (%) of different sampling methods. Symbol † indicates using training sets with annotated reasoning chains. Method MultiArith GSM8K AQuA Zero-Shot-CoT 78.7 40.7 33.5 Manual-CoT 91.7 46.9 35.8† Random-Q-CoT 86.2 47.6† 36.2† Retrieval-Q-CoT 82.8 48.0† 39....