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Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance.
Goal constructs in psychology: Structure, process, and content
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Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W Cohen. 2022 · 2022
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Dheeru Dua, Shivanshu Gupta, Sameer Singh, and Matt Gardner. 2022 · 2022
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Decomposed prompting: A modular approach for solving complex tasks
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Planning with large language models via corrective re-prompting
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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A survey of deep learning for mathematical reasoning
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MAF: Multi-aspect feedback for improving reasoning in large language models
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Chain-of-thought prompting elicits reasoning in large language models
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Least-to-most prompting enables complex reasoning in large language models
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Logical reasoning over natural language as knowledge representation: A survey
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Interpretable math word problem solution generation via step-by-step planning
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Verify-and-edit: A knowledge-enhanced chain-of-thought framework
Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, and Lidong Bing. 2023 · 2023
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Prompt design and engineering: Introduction and advanced methods
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