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Prompt engineering for text generation: Difference between revisions
Prompt engineering for text generation (view source)
Revision as of 00:06, 6 April 2023
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====Zero-shot CoT==== | ====Zero-shot CoT==== | ||
[[Zero-shot CoT prompting]] uses natural language statements, such as "Let's think step by step" or "Let's work this out step by step to be sure we have the right answer," to explicitly encourage the model to generate reasoning chains. Following this, a statement like "Therefore, the answer is" is used to prompt the model to produce the final answer | [[Zero-shot CoT prompting]] uses natural language statements, such as "Let's think step by step" or "Let's work this out step by step to be sure we have the right answer," to explicitly encourage the model to generate reasoning chains. Following this, a statement like "Therefore, the answer is" is used to prompt the model to produce the final answer.<ref name="”128”">Kojima et al. (2022) Large Language Models are Zero-Shot Reasoners https://arxiv.org/abs/2205.11916</ref><ref name="”129”">Zhou et al. (2022) Large Language Models Are Human-Level Prompt Engineers https://arxiv.org/abs/2211.01910</ref> | ||
===Tips and Extensions=== | ===Tips and Extensions=== | ||
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*Ye & Durrett (2022) observed that including explanations in prompts has a small to moderate effect on [[NLP]] tasks that involve reasoning over text, such as [[question-answering]] (QA) and [[natural language inference]] (NLI). They also noted that nonfactual explanations are more likely to lead to incorrect predictions than inconsistent explanations.<ref name="”124”">Ye & Durrett (2022) The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning https://arxiv.org/abs/2205.03401</ref> | *Ye & Durrett (2022) observed that including explanations in prompts has a small to moderate effect on [[NLP]] tasks that involve reasoning over text, such as [[question-answering]] (QA) and [[natural language inference]] (NLI). They also noted that nonfactual explanations are more likely to lead to incorrect predictions than inconsistent explanations.<ref name="”124”">Ye & Durrett (2022) The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning https://arxiv.org/abs/2205.03401</ref> | ||
*[[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> | *[[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== | ==Prompt Engineering for Code Generation Models== | ||