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Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on Large Language Models (LLMs), yielding many disparate code bases.
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Chase, H · 2022
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Rlprompt: Optimizing discrete text prompts with reinforcement learning
Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H., Shu, T., Song, M., Xing, E. P., and Hu, Z · 2022
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Kirsch, L. and Schmidhuber, J · 2022
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Kirsch, L., Harrison, J., Sohl-Dickstein, J., and Metz, L · 2022
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Large language models are zero-shot reasoners
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Algorithm of thoughts: Enhancing exploration of ideas in large language models
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Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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React: Synergizing reasoning and acting in language models
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Socratic models: Composing zero-shot multimodal reasoning with language
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Graph of thoughts: Solving elaborate problems with large language models
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Tree of thoughts: Deliberate problem solving with large language models
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Exchange-of-thought: Enhancing large language model capabilities through cross-model communication
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LangGraph
langchain ai · 2024
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