Awesome Prompt Engineering π§ββοΈ
This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc
Prompt Engineering Course is coming soon..
Prompt Engineering Course is coming soon..
Table of Contents
- Papers
- Tools & Code
- Apis
- Datasets
- Models
- AI Content Detectors
- Educational
- Videos
- Books
- Communities
- How to Contribute
Papers
π
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Prompt Engineering Techniques:
- 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)
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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)
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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)
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Applications of Language Models:
- 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)
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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)
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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)
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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)
- DALLΒ·E: Creating Images from Text [2021] (Arxiv)
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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)
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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)
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Overviews:
Tools & Code
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| Name | Description | Url |
|---|---|---|
| GPT Index | GPT Index is a project consisting of a set of data structures designed to make it easier to use large external knowledge bases with LLMs. | [Github] |
| Promptify | Solve NLP Problems with LLM's & Easily generate different NLP Task prompts for popular generative models like GPT, PaLM, and more with Promptify | [Github] |
| Arize-Phoenix | Open-source tool for ML observability that runs in your notebook environment. Monitor and fine tune LLM, CV and Tabular Models. | [Github] |
| Better Prompt | Test suite for LLM prompts before pushing them to PROD | [Github] |
| Embedchain | Framework to create ChatGPT like bots over your dataset | [Github] |
| Interactive Composition Explorerx | ICE is a Python library and trace visualizer for language model programs. | [Github] |
| Haystack | Open source NLP framework to interact with your data using LLMs and Transformers. | [Github] |
| LangChainx | Building applications with LLMs through composability | [Github] |
| OpenPrompt | An Open-Source Framework for Prompt-learning | [Github] |
| Prompt Engine | This repo contains an NPM utility library for creating and maintaining prompts for Large Language Models (LLMs). | [Github] |
| PromptInject | PromptInject is a framework that assembles prompts in a modular fashion to provide a quantitative analysis of the robustness of LLMs to adversarial prompt attacks. | [Github] |
| Prompts AI | Advanced playground for GPT-3 | [Github] |
| Prompt Source | PromptSource is a toolkit for creating, sharing and using natural language prompts. | [Github] |
| ThoughtSource | A framework for the science of machine thinking | [Github] |
| PROMPTMETHEUS | One-shot Prompt Engineering Toolkit | [Tool] |
Apis
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| Name | Description | Url | Paid or Open-Source |
|---|---|---|---|
| OpenAI | GPT-n for natural language tasks, Codex for translates natural language to code, and DALLΒ·E for creates and edits original images | [OpenAI] | Paid |
| CohereAI | Cohere provides access to advanced Large Language Models and NLP tools through one API | [CohereAI] | Paid |
| Anthropic | Coming soon | [Anthropic] | Paid |
| FLAN-T5 XXL | Coming soon | [HugginFace] | Open-Source |
Datasets
πΎ
| Name | Description | Url |
|---|---|---|
| P3 (Public Pool of Prompts) | P3 (Public Pool of Prompts) is a collection of prompted English datasets covering a diverse set of NLP tasks. | [HuggingFace] |
| Awesome ChatGPT Prompts | Repo includes ChatGPT prompt curation to use ChatGPT better. | [Github] |
| Writing Prompts | Collection of a large dataset of 300K human-written stories paired with writing prompts from an online forum(reddit) | [Kaggle] |
| Midjourney Prompts | Text prompts and image URLs scraped from MidJourney's public Discord server | [HuggingFace] |
Models
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| Name | Description | Url |
|---|---|---|
| ChatGPT | ChatGPT | [OpenAI] |
| Codex | The Codex models are descendants of our GPT-3 models that can understand and generate code. Their training data contains both natural language and billions of lines of public code from GitHub | [Github] |
| Bloom | BigScience Large Open-science Open-access Multilingual Language Model | [HuggingFace] |
| Facebook LLM | OPT-175B is a GPT-3 equivalent model trained by Meta. It is by far the largest pretrained language model available with 175 billion parameters. | [Alpa] |
| GPT-NeoX | GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile | [HuggingFace] |
| FLAN-T5 XXL | Flan-T5 is an instruction-tuned model, meaning that it exhibits zero-shot-like behavior when given instructions as part of the prompt. | [HuggingFace/Google] |
| XLM-RoBERTa-XL | XLM-RoBERTa-XL model pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages. | [HuggingFace] |
| GPT-J | It is a GPT-2-like causal language model trained on the Pile dataset | [HuggingFace] |
| PaLM-rlhf-pytorch | Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM | [Github] |
| GPT-Neo | An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library. | [Github] |
| LaMDA-rlhf-pytorch | Open-source pre-training implementation of Google's LaMDA in PyTorch. Adding RLHF similar to ChatGPT. | [Github] |
| RLHF | Implementation of Reinforcement Learning from Human Feedback (RLHF) | [Github] |
| GLM-130B | GLM-130B: An Open Bilingual Pre-Trained Model | [Github] |
AI Content Detectors
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| Name | Description | Url |
|---|---|---|
| AI Text Classifier | The AI Text Classifier is a fine-tuned GPT model that predicts how likely it is that a piece of text was generated by AI from a variety of sources, such as ChatGPT. | [OpenAI] |
| GPT-2 Output Detector | This is an online demo of the GPT-2 output detector model, based on the π€/Transformers implementation of RoBERTa. | [HuggingFace] |
| Openai Detector | AI classifier for indicating AI-written text (OpenAI Detector Python wrapper) | [GitHub] |
Courses
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Tutorials
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Introduction to Prompt Engineering
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Beginner's Guide to Generative Language Models
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Best Practices for Prompt Engineering
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Complete Guide to Prompt Engineering
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Technical Aspects of Prompt Engineering
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Resources for Prompt Engineering
Videos
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- 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
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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!