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--- nlp:prompt_engineering [2025/03/22 02:18] – [Introductions and Overviews] jmflanig
+++ nlp:prompt_engineering [2025/10/08 09:16] (current) – [Examples of Prompts] jmflanig
@@ Line 7: / Line 7: @@
     * [[https://www.promptingguide.ai/|Prompt Engineering Guide]] This one is pretty good
     * [[https://cloud.google.com/discover/what-is-prompt-engineering|Google Cloud - Prompt engineering: overview and guide]]
+  * **Overview Papers**
+    * [[https://aclanthology.org/2023.findings-emnlp.618.pdf|Leidinger et al 2023 - The language of prompting: What linguistic properties make a prompt successful?]]
   * Slides
   * Blogs:
@@ Line 23: / Line 25: @@
   * [[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/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/2306.01150.pdf|Yin et al 2023 - Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning]]
@@ Line 29: / Line 32: @@
   * [[https://arxiv.org/abs/2308.05342|Wang & Zhao 2023 - Metacognitive Prompting Improves Understanding
 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?**
+    * [[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 ====
@@ Line 52: / Line 59: @@
   * [[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]]
+  * 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 =====
   * [[Prompting]]