Least-to-Most Prompting Enables Complex Reasoning In Large Language Modelshttps://arxiv.org/abs/2205.10625 Least-to-Most Prompting Enables Complex Reasoning in Large Language ModelsChain-of-thought prompting has demonstrated remarkable performance on various natural language reasoning tasks. However, it tends to perform poorly on tasks which requires solving problems harder than the exemplars sh..

Large Language Models are Zero-Shot Reasonershttps://arxiv.org/abs/2205.11916 Large Language Models are Zero-Shot ReasonersPretrained large language models (LLMs) are widely used in many sub-fields of natural language processing (NLP) and generally known as excellent few-shot learners with task-specific exemplars. Notably, chain of thought (CoT) prompting, a recent technique farxiv.org Overview..

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models https://arxiv.org/abs/2201.11903 Chain-of-Thought Prompting Elicits Reasoning in Large Language ModelsWe explore how generating a chain of thought -- a series of intermediate reasoning steps -- significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning ab..

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