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

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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)

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

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

1.1概述类Overviews

1.2方法类Approaches

1.3应用Applications

1.4 合集类Collections

2.prompt工具和库

3.数据集

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