Prompt engineering for text generation: Difference between revisions

*[[Self-Ask]], a method proposed by Press et al. (2022), repeatedly prompts the model to ask follow-up questions, constructing the thought process iteratively.<ref name="”125”">Press et al. (2022) Measuring and Narrowing the Compositionality Gap in Language Models https://arxiv.org/abs/2210.03350</ref> Search engine results can be used to answer these follow-up questions. Similarly, [[IRCoT]] ([[Interleaving Retrieval CoT]]; Trivedi et al. 2022) and [[ReAct]] ([[Reason + Act]]; Yao et al. 2023) combine iterative CoT prompting with queries to Wikipedia APIs. These methods search for relevant entities and content and then incorporate the retrieved information back into the context, further enhancing the model's reasoning capabilities.<ref name="”126”">Trivedi et al. (2022) Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions https://arxiv.org/abs/2212.10509</ref><ref name="”127”">Yao et al. (2023) ReAct: Synergizing Reasoning and Acting in Language Models https://arxiv.org/abs/2210.03629</ref>

==Prompt Engineering for Code Generation Models==