Awesome-Prompt-Engineering

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This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc

AI summary

Prompting library

A curated collection of resources and research papers on designing effective prompts for large language models, including GPT and other transformer-based architectures.

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147 links in 10 sections, with live GitHub stats.activeno commit in 2y

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)

Tools & Code

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