Table of Contents / Papers |
| 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 | | | |
| 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) |
| 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) |
| 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) |
| 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 | | | |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| 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) |
| Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? | | | [2022] (Arxiv) |
Table of Contents / Tools & Code |
| [Github] | 37,371 | 11 months ago | |
| [Github] | 3,327 | over 1 year ago | |
| [Github] | 4,271 | 11 months ago | |
| [Github] | 43 | over 1 year ago | |
| [Github] | 2,588 | 11 months ago | |
| [Github] | 23,331 | 11 months ago | |
| [Github] | 537 | about 1 year ago | |
| [Github] | 18,094 | 11 months ago | |
| [Github] | 96,146 | 11 months ago | |
| [Github] | 4,398 | over 1 year ago | |
| [Github] | 2,602 | over 2 years ago | |
| [Github] | 318 | over 1 year ago | |
| [Github] | 397 | almost 3 years ago | |
| [Github] | 2,718 | about 2 years ago | |
| [Github] | 906 | 11 months ago | |
| [Tool] | | | |
| [Github] | 969 | over 1 year ago | |
| [Tool] | | | |
| [Tool] | | | |
| [Github] | 1,624 | 11 months ago | |
| [Tool] | | | |
Table of Contents / Apis |
| [OpenAI] | | | |
| [CohereAI] | | | |
| [Anthropic] | | | |
| [HuggingFace] | | | |
Table of Contents / Datasets |
| [HuggingFace] | | | |
| [Github] | 114,113 | 12 months ago | |
| [Kaggle] | | | |
| [HuggingFace] | | | |
Table of Contents / Models |
| [OpenAI] | | | |
| [Github] | | | |
| [HuggingFace] | | | |
| [Alpa] | | | |
| [HuggingFace] | | | |
| [HuggingFace/Google] | | | |
| [HuggingFace] | | | |
| [HuggingFace] | | | |
| [Github] | 7,729 | almost 2 years ago | |
| [Github] | 8,244 | over 3 years ago | |
| [Github] | 467 | over 1 year ago | |
| [Github] | 171 | over 2 years ago | |
| [Github] | 7,672 | over 2 years ago | |
| [HuggingFace] | | | |
Table of Contents / AI Content Detectors |
| [OpenAI] | | | |
| [HuggingFace] | | | |
| [GitHub] | 122 | over 2 years ago | |
Table of Contents / Courses |
| ChatGPT Prompt Engineering for Developers | | | , by |
| Prompt Engineering for Vision Models | | | by |
Table of Contents / Tutorials |
| Prompt Engineering 101 - Introduction and resources | | | |
| Prompt Engineering 101 | | | |
| Prompt Engineering Guide by SudalaiRajkumar | 44 | about 1 year ago | |
| A beginner-friendly guide to generative language models - LaMBDA guide | | | |
| Generative AI with Cohere: Part 1 - Model Prompting | | | |
| Best practices for prompt engineering with OpenAI API | | | |
| How to write good prompts | | | |
| A Complete Introduction to Prompt Engineering for Large Language Models | | | |
| Prompt Engineering Guide: How to Engineer the Perfect Prompts | | | |
| 3 Principles for prompt engineering with GPT-3 | | | |
| A Generic Framework for ChatGPT Prompt Engineering | | | |
| Methods of prompt programming | | | |
| Awesome ChatGPT Prompts | 114,113 | 12 months ago | |
| Best 100+ Stable Diffusion Prompts | | | |
| DALLE Prompt Book | | | |
| OpenAI Cookbook | 60,643 | 11 months ago | |
| Prompt Engineering by Microsoft | | | |
Table of Contents / 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 | | | |
Table of Contents / Communities |
| OpenAI Discord | | | |
| PromptsLab Discord | | | |
| Learn Prompting | | | |
| r/ChatGPT Discord | | | |
| MidJourney Discord | | | |