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nlp:prompt_engineering

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nlp:prompt_engineering [2025/03/24 07:46] – [Papers] jmflanignlp:prompt_engineering [2025/10/08 09:16] (current) – [Examples of Prompts] jmflanig
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   * [[https://arxiv.org/pdf/2109.01247.pdf|Webson & Pavlick 2021 - Do Prompt-Based Models Really Understand the Meaning of Their Prompts?]]   * [[https://arxiv.org/pdf/2109.01247.pdf|Webson & Pavlick 2021 - Do Prompt-Based Models Really Understand the Meaning of Their Prompts?]]
   * **[[https://arxiv.org/pdf/2212.10560.pdf|Wang et al 2022 - Self-Instruct: Aligning Language Model with Self Generated Instructions]]**   * **[[https://arxiv.org/pdf/2212.10560.pdf|Wang et al 2022 - Self-Instruct: Aligning Language Model with Self Generated Instructions]]**
 +  * [[https://arxiv.org/pdf/2301.07085|Webson et al 2023 - Are Language Models Worse than Humans at Following Prompts? It's Complicated]]
   * [[https://arxiv.org/pdf/2301.08721.pdf|Cheng et al 2023 - Batch Prompting: Efficient Inference with Large Language Model APIs]] Batch prompting to save money   * [[https://arxiv.org/pdf/2301.08721.pdf|Cheng et al 2023 - Batch Prompting: Efficient Inference with Large Language Model APIs]] Batch prompting to save money
   * [[https://arxiv.org/pdf/2306.01150.pdf|Yin et al 2023 - Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning]]   * [[https://arxiv.org/pdf/2306.01150.pdf|Yin et al 2023 - Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning]]
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   * [[https://arxiv.org/abs/2308.05342|Wang & Zhao 2023 - Metacognitive Prompting Improves Understanding   * [[https://arxiv.org/abs/2308.05342|Wang & Zhao 2023 - Metacognitive Prompting Improves Understanding
 in Large Language Models]] in Large Language Models]]
 +  * [[https://arxiv.org/pdf/2503.10084|Zhang et al 2025 - Why Prompt Design Matters and Works: A Complexity Analysis of Prompt Search Space in LLMs]] Shows that naive CoT prompting, like “think step by step,"can severely hinder performance.
   * **What makes a good prompt?**   * **What makes a good prompt?**
     * [[https://aclanthology.org/2023.findings-emnlp.618.pdf|Leidinger et al 2023 - The language of prompting: What linguistic properties make a prompt successful?]]     * [[https://aclanthology.org/2023.findings-emnlp.618.pdf|Leidinger et al 2023 - The language of prompting: What linguistic properties make a prompt successful?]]
 +    * [[https://aclanthology.org/2023.findings-emnlp.679.pdf|Gonen et al 2023 - Demystifying Prompts in Language Models via Perplexity Estimation]]
  
 ==== Automatic Prompt Engineering ==== ==== Automatic Prompt Engineering ====
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   * [[https://arxiv.org/pdf/2210.02441.pdf|Arora et al 2022 - Ask Me Anything: A Simple Strategy for Prompting Language Models]]   * [[https://arxiv.org/pdf/2210.02441.pdf|Arora et al 2022 - Ask Me Anything: A Simple Strategy for Prompting Language Models]]
   * [[https://github.com/f/awesome-chatgpt-prompts|Awesome ChatGPT Prompts]]   * [[https://github.com/f/awesome-chatgpt-prompts|Awesome ChatGPT Prompts]]
 +  * Prompt to break down sentences into independent facts:
 +{{media:facts-prompt-arxiv-2305.14251.png}}
 +(from [[https://arxiv.org/pdf/2305.14251.pdf|Min 2023]])
 +  * [[https://arxiv.org/pdf/2404.07972|Xie et al 2024 - OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments ]] See the prompts in appendix C
 +
  
 ===== Related Pages ===== ===== Related Pages =====
   * [[Prompting]]   * [[Prompting]]
  
nlp/prompt_engineering.1742802384.txt.gz · Last modified: 2025/03/24 07:46 by jmflanig
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