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Table of Contents

• Papers
• Tools & Code
• Apis
• Datasets
• Models
• AI Content Detectors
• Educational
  • Courses
  • Tutorials
• Videos
• Books
• Communities
• How to Contribute

Papers

📄

• Prompt Engineering Techniques:

  • Text Mining for Prompt Engineering: Text-Augmented Open Knowledge Graph Completion via PLMs [2023] (ACL)

  • A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT [2023] (Arxiv)

  • Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery [2023] (Arxiv)

  • Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models [2023] (Arxiv)

  • Progressive Prompts: Continual Learning for Language Models [2023] (Arxiv)

  • Batch Prompting: Efficient Inference with LLM APIs [2023] (Arxiv)

  • Successive Prompting for Decompleting Complex Questions [2022] (Arxiv)

  • Structured Prompting: Scaling In-Context Learning to 1,000 Examples [2022] (Arxiv)

  • Large Language Models Are Human-Level Prompt Engineers [2022] (Arxiv)

  • Ask Me Anything: A simple strategy for prompting language models [2022] (Arxiv)

  • Prompting GPT-3 To Be Reliable 2022

  • Decomposed Prompting: A Modular Approach for Solving Complex Tasks [2022] (Arxiv)

  • PromptChainer: Chaining Large Language Model Prompts through Visual Programming [2022] (Arxiv)

  • Investigating Prompt Engineering in Diffusion Models [2022] (Arxiv)

  • Show Your Work: Scratchpads for Intermediate Computation with Language Models [2021] (Arxiv)

  • Reframing Instructional Prompts to GPTk's Language [2021] (Arxiv)

  • Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity [2021] (Arxiv)

  • The Power of Scale for Parameter-Efficient Prompt Tuning [2021] (Arxiv)

  • Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm [2021] (Arxiv)

  • Prefix-Tuning: Optimizing Continuous Prompts for Generation [2021] (Arxiv)

• Reasoning and In-Context Learning:

  • Multimodal Chain-of-Thought Reasoning in Language Models [2023] (Arxiv)

  • On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning [2022] (Arxiv)

  • ReAct: Synergizing Reasoning and Acting in Language Models [2022] (Arxiv)

  • Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought [2022] (Arxiv)

  • On the Advance of Making Language Models Better Reasoners [2022] (Arxiv)

  • Large Language Models are Zero-Shot Reasoners [2022] (Arxiv)

  • Reasoning Like Program Executors [2022] (Arxiv)

  • Self-Consistency Improves Chain of Thought Reasoning in Language Models [2022] (Arxiv)

  • Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? [2022] (Arxiv)

  • Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering [2022] (Arxiv)

  • Chain of Thought Prompting Elicits Reasoning in Large Language Models [2021] (Arxiv)

  • Generated Knowledge Prompting for Commonsense Reasoning [2021] (Arxiv)

  • BERTese: Learning to Speak to BERT [2021] (Acl)

• Evaluating and Improving Language Models:

  • Large Language Models Can Be Easily Distracted by Irrelevant Context [2023] (Arxiv)

  • Crawling the Internal Knowledge-Base of Language Models [2023] (Arxiv)

  • Discovering Language Model Behaviors with Model-Written Evaluations [2022] (Arxiv)

  • Calibrate Before Use: Improving Few-Shot Performance of Language Models [2021] (Arxiv)

• Applications of Language Models:

  • Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves [2023] (Arxiv)

  • Prompting for Multimodal Hateful Meme Classification [2023] (Arxiv)

  • PLACES: Prompting Language Models for Social Conversation Synthesis [2023] (Arxiv)

  • Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation [2023] (Arxiv)

  • PAL: Program-aided Language Models 2023

  • Legal Prompt Engineering for Multilingual Legal Judgement Prediction [2023] (Arxiv)

  • Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language [2022] (Arxiv)

  • Plot Writing From Scratch Pre-Trained Language Models [2022] (Acl)

  • AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts [2020] (Arxiv)

• Threat Detection and Adversarial Examples:

  • Constitutional AI: Harmlessness from AI Feedback [2022] (Arxiv)

  • Ignore Previous Prompt: Attack Techniques For Language Models [2022] (Arxiv)

  • Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods [2022] (Arxiv)

  • Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples [2022] (Arxiv)

  • Toxicity Detection with Generative Prompt-based Inference [2022] (Arxiv)

  • How Can We Know What Language Models Know? [2020] (Mit)

• Few-shot Learning and Performance Optimization:

  • Promptagator: Few-shot Dense Retrieval From 8 Examples [2022] (Arxiv)

  • The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning [2022] (Arxiv)

  • Making Pre-trained Language Models Better Few-shot Learners [2021] (Acl)

  • Language Models are Few-Shot Learners [2020] (Arxiv)

• Text to Image Generation:

  • A Taxonomy of Prompt Modifiers for Text-To-Image Generation [2022] (Arxiv)
  • Design Guidelines for Prompt Engineering Text-to-Image Generative Models [2021] (Arxiv)
  • High-Resolution Image Synthesis with Latent Diffusion Models [2021] (Arxiv)
  • DALL·E: Creating Images from Text [2021] (Arxiv)

• Text to Music/Sound Generation:

  • MusicLM: Generating Music From Text [2023] (Arxiv)
  • ERNIE-Music: Text-to-Waveform Music Generation with Diffusion Models [2023] (Arxiv)
  • Noise2Music: Text-conditioned Music Generation with Diffusion Models [2023) (Arxiv)
  • AudioLM: a Language Modeling Approach to Audio Generation [2023] (Arxiv)
  • Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models [2023] (Arxiv)

• Text to Video Generation:

  • Dreamix: Video Diffusion Models are General Video Editors [2023] (Arxiv)

  • Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation [2022] (Arxiv)

  • Noise2Music: Text-conditioned Music Generation with Diffusion Models [2023) (Arxiv)

  • AudioLM: a Language Modeling Approach to Audio Generation [2023] (Arxiv)

• Overviews:

  • Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? [2022] (Arxiv)

Tools & Code

🔧

Apis

💻

Datasets

💾

Models

🧠

AI Content Detectors

🔎

Courses

👩‍🏫

• ChatGPT Prompt Engineering for Developers, by deeplearning.ai
• Prompt Engineering for Vision Models by DeepLearning.AI

Tutorials

📚

• Introduction to Prompt Engineering

  • Prompt Engineering 101 - Introduction and resources
  • Prompt Engineering 101
  • Prompt Engineering Guide by SudalaiRajkumar

• Beginner's Guide to Generative Language Models

  • A beginner-friendly guide to generative language models - LaMBDA guide
  • Generative AI with Cohere: Part 1 - Model Prompting

• Best Practices for Prompt Engineering

  • Best practices for prompt engineering with OpenAI API
  • How to write good prompts

• Complete Guide to Prompt Engineering

  • A Complete Introduction to Prompt Engineering for Large Language Models
  • Prompt Engineering Guide: How to Engineer the Perfect Prompts

• Technical Aspects of Prompt Engineering

  • 3 Principles for prompt engineering with GPT-3
  • A Generic Framework for ChatGPT Prompt Engineering
  • Methods of prompt programming

• Resources for Prompt Engineering

  • Awesome ChatGPT Prompts
  • Best 100+ Stable Diffusion Prompts
  • DALLE Prompt Book
  • OpenAI Cookbook
  • Prompt Engineering by Microsoft

Videos

🎥

• Advanced ChatGPT Prompt Engineering
• ChatGPT: 5 Prompt Engineering Secrets For Beginners
• CMU Advanced NLP 2022: Prompting
• Prompt Engineering - A new profession ?
• ChatGPT Guide: 10x Your Results with Better Prompts
• Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP
• Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting

Communities

🤝

• OpenAI Discord
• PromptsLab Discord
• Learn Prompting
• r/ChatGPT Discord
• MidJourney Discord

How to Contribute

We welcome contributions to this list! In fact, that's the main reason why I created it - to encourage contributions and encourage people to subscribe to changes in order to stay informed about new and exciting developments in the world of Large Language Models(LLMs) & Prompt-Engineering.

Before contributing, please take a moment to review our contribution guidelines. These guidelines will help ensure that your contributions align with our objectives and meet our standards for quality and relevance. Thank you for your interest in contributing to this project!