Prompt工程师指南[资料整合篇]：Prompt最新前沿论文整理合集、工具和库推荐、数据集整合、推荐阅读内容等，超全面资料

1.论文合集

The following are the latest papers (sorted by release date) on prompt engineering. We update this on a daily basis and new papers come in. We incorporate summaries of these papers to the guides above every week.

1.1概述类Overviews

• Augmented Language Models: a Survey (Feb 2023)
• A Survey for In-context Learning (Dec 2022)
• Towards Reasoning in Large Language Models: A Survey (Dec 2022)
• Reasoning with Language Model Prompting: A Survey (Dec 2022)
• Emergent Abilities of Large Language Models (Jun 2022)
• A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
• Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing (Jul 2021)

1.2方法类Approaches

• Model-tuning Via Prompts Makes NLP Models Adversarially Robust (Mar 2023)
• Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer (March 2023)
• CoTEVer: Chain of Thought Prompting Annotation Toolkit for Explanation Verification (March 2023)
• Larger language models do in-context learning differently (March 2023)
• OpenICL: An Open-Source Framework for In-context Learning (March 2023)
• Dynamic Prompting: A Unified Framework for Prompt Tuning (March 2023)
• Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning (March 2023)
• Effectiveness of Data Augmentation for Prefix Tuning with Limited Data (March 2023)
• Mixture of Soft Prompts for Controllable Data Generation (March 2023)
• Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners (March 2023)
• How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks (March 2023)
• Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT (Feb 2023)
• EvoPrompting: Language Models for Code-Level Neural Architecture Search (Feb 2023)
• In-Context Instruction Learning (Feb 2023)
• Chain of Hindsight Aligns Language Models with Feedback (Feb 2023)
• Language Is Not All You Need: Aligning Perception with Language Models (Feb 2023)
• Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data (Feb 2023)
• Active Prompting with Chain-of-Thought for Large Language Models (Feb 2023)
• More than you’ve asked for: A Comprehensive Analysis of Novel Prompt Injection Threats to Application-Integrated Large Language Models (Feb 2023)
• A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT (Feb 2023)
• Guiding Large Language Models via Directional Stimulus Prompting (Feb 2023)
• How Does In-Context Learning Help Prompt Tuning? (Feb 2023)
• Scalable Prompt Generation for Semi-supervised Learning with Language Models (Feb 2023)
• Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints (Feb 2023)
• À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting (Feb 2023)
• GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks (Feb 2023)
• The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
• SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains (Feb 2023)
• Evaluating the Robustness of Discrete Prompts (Feb 2023)
• Compositional Exemplars for In-context Learning (Feb 2023)
• Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery (Feb 2023)
• Multimodal Chain-of-Thought Reasoning in Language Models (Feb 2023)
• Large Language Models Can Be Easily Distracted by Irrelevant Context (Feb 2023)
• Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models (Feb 2023)
• Progressive Prompts: Continual Learning for Language Models (Jan 2023)
• Batch Prompting: Efficient Inference with LLM APIs (Jan 2023)
• Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP (Dec 2022)
• On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning (Dec 2022)
• Constitutional AI: Harmlessness from AI Feedback (Dec 2022)
• Successive Prompting for Decomposing Complex Questions (Dec 2022)
• Large Language Models are reasoners with Self-Verification (Dec 2022)
• Discovering Language Model Behaviors with Model-Written Evaluations (Dec 2022)
• Structured Prompting: Scaling In-Context Learning to 1,000 Examples (Dec 2022)
• PAL: Program-aided Language Models (Nov 2022)
• Large Language Models Are Human-Level Prompt Engineers (Nov 2022)
• Ignore Previous Prompt: Attack Techniques For Language Models (Nov 2022)
• Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods (Nov 2022)
• Teaching Algorithmic Reasoning via In-context Learning (Nov 2022)
• Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference (Nov 2022)
• Ask Me Anything: A simple strategy for prompting language models (Oct 2022)
• Recitation-Augmented Language Models (Oct 2022)
• ReAct: Synergizing Reasoning and Acting in Language Models (Oct 2022)
• Prompting GPT-3 To Be Reliable (Oct 2022)
• Decomposed Prompting: A Modular Approach for Solving Complex Tasks (Oct 2022)
• Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought (Oct 2022)
• Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examples (Sep 2022)
• Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning (Sep 2022)
• Promptagator: Few-shot Dense Retrieval From 8 Examples (Sep 2022)
• Atlas: Few-shot Learning with Retrieval Augmented Language Models (Nov 2022)
• DocPrompting: Generating Code by Retrieving the Docs (July 2022)
• On the Advance of Making Language Models Better Reasoners (June 2022)
• Large Language Models are Zero-Shot Reasoners (May 2022)
• Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations (May 2022)
• MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning (May 2022)
• PPT: Pre-trained Prompt Tuning for Few-shot Learning (Mqy 2022)
• Toxicity Detection with Generative Prompt-based Inference (May 2022)
• Learning to Transfer Prompts for Text Generation (May 2022)
• The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning (May 2022)
• A Taxonomy of Prompt Modifiers for Text-To-Image Generation (Apr 2022)
• PromptChainer: Chaining Large Language Model Prompts through Visual Programming (Mar 2022)
• Self-Consistency Improves Chain of Thought Reasoning in Language Models (March 2022)
• Training language models to follow instructions with human feedback
• Rethinking the Role of Demonstrations: What Makes In-Context Learning Work? (Feb 2022)
• Chain of Thought Prompting Elicits Reasoning in Large Language Models (Jan 2022)
• Show Your Work: Scratchpads for Intermediate Computation with Language Models (Nov 2021)
• AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (Oct 2021)
• Generated Knowledge Prompting for Commonsense Reasoning (Oct 2021)
• Multitask Prompted Training Enables Zero-Shot Task Generalization (Oct 2021)
• Reframing Instructional Prompts to GPTk’s Language (Sep 2021)
• Design Guidelines for Prompt Engineering Text-to-Image Generative Models (Sep 2021)
• Making Pre-trained Language Models Better Few-shot Learners (Aug 2021)
• Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity (April 2021)
• BERTese: Learning to Speak to BERT (April 2021)
• The Power of Scale for Parameter-Efficient Prompt Tuning (April 2021)
• Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm (Feb 2021)
• Calibrate Before Use: Improving Few-Shot Performance of Language Models (Feb 2021)
• Prefix-Tuning: Optimizing Continuous Prompts for Generation (Jan 2021)
• Learning to Generate Task-Specific Adapters from Task Description (Jan 2021)
• Making Pre-trained Language Models Better Few-shot Learners (Dec 2020)
• Learning from Task Descriptions (Nov 2020)
• AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (Oct 2020)
• Language Models are Few-Shot Learners (May 2020)
• How Can We Know What Language Models Know? (July 2020)

1.3应用Applications

• Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses? (Mar 2023)
• SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models (Mar 2023)
• ICL-D3IE: In-Context Learning with Diverse Demonstrations Updating for Document Information Extraction (March 2023)
• MathPrompter: Mathematical Reasoning using Large Language Models (March 2023)
• Prompt-Based Learning for Thread Structure Prediction in Cybersecurity Forums (March 2023)
• Choice Over Control: How Users Write with Large Language Models using Diegetic and Non-Diegetic Prompting (March 2023)
• Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering (March 2023)
• Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis (March 2023)
• SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks (March 2023)
• Goal Driven Discovery of Distributional Differences via Language Descriptions (Feb 2023)
• Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models (Feb 2023)
• TabGenie: A Toolkit for Table-to-Text Generation (Feb 2023)
• SGL-PT: A Strong Graph Learner with Graph Prompt Tuning (Feb 2023)
• Few-Shot Table-to-Text Generation with Prompt-based Adapter (Feb 2023)
• Language Models Are Few-shot Learners for Prognostic Prediction (Feb 2023)
• STA: Self-controlled Text Augmentation for Improving Text Classifications (Feb 2023)
• Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback (Feb 2023)
• How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Study (Feb 2023)
• Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales (Feb 2023)
• LabelPrompt: Effective Prompt-based Learning for Relation Classification (Feb 2023)
• Language Model Crossover: Variation through Few-Shot Prompting (Feb 2023)
• Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition (Feb 2023)
• The Capacity for Moral Self-Correction in Large Language Models (Feb 2023)
• Prompting for Multimodal Hateful Meme Classification (Feb 2023)
• PLACES: Prompting Language Models for Social Conversation Synthesis (Feb 2023)
• Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generation (Feb 2023)
• Crawling the Internal Knowledge-Base of Language Models (Jan 2023)
• Legal Prompt Engineering for Multilingual Legal Judgement Prediction (Dec 2022)
• Investigating Prompt Engineering in Diffusion Models (Nov 2022)
• Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering (Sep 2022)
• Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using Natural Language (Oct 2022)
• Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? (Oct 2022)
• Plot Writing From Scratch Pre-Trained Language Models (July 2022)

1.4 合集类Collections

• Chain-of-Thought Papers
• Papers with Code
• Prompt Papers

2.prompt工具和库

• AI Test Kitchen
• betterprompt
• ClickPrompt
• DreamStudio
• DUST
• Dyno
• EmergentMind
• EveryPrompt
• GPT Index
• GPTTools
• hwchase17/adversarial-prompts
• Interactive Composition Explorer
• LangChain
• Lexica
• loom
• Metaprompt
• OpenAI Playground
• OpenICL
• OpenPrompt
• OpenPlayground
• Playground
• Prodia
• Prompt Base
• Prompt Engine
• Prompt Generator for OpenAI’s DALL-E 2
• Promptable
• PromptInject
• Prompts.ai
• PromptPerfect
• Promptly
• PromptSource
• Promptist
• Scale SpellBook
• sharegpt
• ThoughtSource
• Visual Prompt Builder

3.数据集

• Anthropic’s Red Team dataset, (paper)
• Awesome ChatGPT Prompts
• DiffusionDB
• Midjourney Prompts
• P3 - Public Pool of Prompts
• PartiPrompts
• Real Toxicity Prompts
• Stable Diffusion Dataset
• WritingPrompts

推荐读物

• 3 Principles for prompt engineering with GPT-3
• A beginner-friendly guide to generative language models - LaMBDA guide
• A Complete Introduction to Prompt Engineering for Large Language Models
• A Generic Framework for ChatGPT Prompt Engineering
• An SEO’s guide to ChatGPT prompts
• AI Content Generation
• AI’s rise generates new job title: Prompt engineer
• Awesome ChatGPT Prompts
• Best 100+ Stable Diffusion Prompts
• Best practices for prompt engineering with OpenAI API
• Building GPT-3 applications — beyond the prompt
• Can AI really be protected from text-based attacks?
• ChatGPT, AI and GPT-3 Apps and use cases
• ChatGPT Prompts
• CMU Advanced NLP 2022: Prompting
• Common Sense as Dark Matter - Yejin Choi | Stanford MLSys #78
• Curtis64’s set of prompt gists
• DALL·E 2 Prompt Engineering Guide
• DALL·E 2 Preview - Risks and Limitations
• DALLE Prompt Book
• DALL-E, Make Me Another Picasso, Please
• Diffusion Models: A Practical Guide
• Exploiting GPT-3 Prompts
• Exploring Prompt Injection Attacks
• Extrapolating to Unnatural Language Processing with GPT-3’s In-context Learning: The Good, the Bad, and the Mysterious
• Generative AI with Cohere: Part 1 - Model Prompting
• Get a Load of This New Job: “Prompt Engineers” Who Act as Psychologists to AI Chatbots
• Giving GPT-3 a Turing Test
• GPT-3 & Beyond
• GPT3 and Prompts: A quick primer
• Hands-on with Bing’s new ChatGPT-like features
• How to Draw Anything
• How to get images that don’t suck
• How to make LLMs say true things
• How to perfect your prompt writing for AI generators
• How to write good prompts
• If I Was Starting Prompt Engineering in 2023: My 8 Insider Tips
• Indirect Prompt Injection on Bing Chat
• Interactive guide to GPT-3 prompt parameters
• Introduction to Reinforcement Learning with Human Feedback
• In defense of prompt engineering
• JailBreaking ChatGPT: Everything You Need to Know
• Language Models and Prompt Engineering: Systematic Survey of Prompting Methods in NLP
• Learn Prompting
• Methods of prompt programming
• Mysteries of mode collapse
• NLP for Text-to-Image Generators: Prompt Analysis
• NLP with Deep Learning CS224N/Ling284 - Lecture 11: Promting, Instruction Tuning, and RLHF
• Notes for Prompt Engineering by sw-yx
• OpenAI Cookbook
• OpenAI Prompt Examples for several applications
• Pretrain, Prompt, Predict - A New Paradigm for NLP
• Prompt Engineer: Tech’s hottest job title?
• Prompt Engineering 101 - Introduction and resources
• Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting
• Prompt Engineering 101
• Prompt Engineering - A new profession ?
• Prompt Engineering by co:here
• Prompt Engineering by Microsoft
• Prompt Engineering: The Career of Future
• Prompt engineering davinci-003 on our own docs for automated support (Part I)
• Prompt Engineering Guide: How to Engineer the Perfect Prompts
• Prompt Engineering in GPT-3
• Prompt Engineering Template
• Prompt Engineering Topic by GitHub
• Prompt Engineering: The Ultimate Guide 2023 [GPT-3 & ChatGPT]
• Prompt Engineering: From Words to Art
• Prompt Engineering with OpenAI’s GPT-3 and other LLMs
• Prompt injection attacks against GPT-3
• Prompt injection to read out the secret OpenAI API key
• Prompting: Better Ways of Using Language Models for NLP Tasks
• Prompting for Few-shot Learning
• Prompting in NLP: Prompt-based zero-shot learning