An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.
📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt
⭐️ Shining ⭐️: This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness.
The resources include:
🎉Papers🎉: The latest papers about in-context learning or prompt engineering.
🎉Playground🎉: Large language models that enable prompt experimentation.
🎉Prompt Engineering🎉: Prompt techniques for leveraging large language models.
🎉ChatGPT Prompt🎉: Prompt examples that can be applied in our work and daily lives.
In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk):
- Those who enhance their abilities through the use of AI;
- Those whose jobs are replaced by AI automation.
💎EgoAlpha: Hello! human👤, are you ready?
-
[2023.3.15] Two Breaking News:
-
[2023.3.13] LLaMA has been fine-tuned by Stanford
-
[2023.3.10] Announcing OpenChatKit by Together
-
[2023.3.9] GPT-4 is coming next week and it will be multimodal,announced by OpenAI.
-
[2023.3.8] Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models
-
[2023.3.7] Larger language models do in-context learning differently
-
[2023.3.6] Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning
Augmented Language Models: a Survey （2023.02.15）
A Survey for In-context Learning （2022.12.31）
Towards Reasoning in Large Language Models: A Survey （2022.12.20）
Reasoning with Language Model Prompting: A Survey （2022.12.19）
Emergent Abilities of Large Language Models （2022.06.15）
Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing （2021.07.28）
👉Complete paper list 🔗 for "Survey"👈
Progressive Prompts: Continual Learning for Language Models （2023.01.29）
Batch Prompting: Efficient Inference with Large Language Model APIs （2023.01.19）
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding （2022.10.16）
Promptagator: Few-shot Dense Retrieval From 8 Examples （2022.09.23）
Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models （2022.08.16）
DocPrompting: Generating Code by Retrieving the Docs （2022.07.13）
Design Guidelines for Prompt Engineering Text-to-Image Generative Models （2021.09.14）
Program Synthesis with Large Language Models （2021.08.16）
PTR: Prompt Tuning with Rules for Text Classification （2021.05.24）
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains （2021.02.24）
👉Complete paper list 🔗 for "Prompt Design"👈
Guiding Large Language Models via Directional Stimulus Prompting （2023.02.22）
Evaluating the Robustness of Discrete Prompts （2023.02.11）
Making Pre-trained Language Models Better Few-shot Learners （2021.01.01）
Eliciting Knowledge from Language Models Using Automatically Generated Prompts （2020.10.29）
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification （2020.10.26）
👉Complete paper list 🔗 for "Automatic Prompt"👈
Multimodal Chain-of-Thought Reasoning in Language Models （2023.02.02）
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models （2023.02.01）
Faithful Chain-of-Thought Reasoning （2023.01.31）
Large Language Models Are Reasoning Teachers （2022.12.20）
The Impact of Symbolic Representations on In-context Learning for Few-shot Reasoning （2022.12.16）
Complementary Explanations for Effective In-Context Learning （2022.11.25）
Prompting GPT-3 To Be Reliable （2022.10.17）
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them （2022.10.17）
Automatic Chain of Thought Prompting in Large Language Models （2022.10.07）
Measuring and Narrowing the Compositionality Gap in Language Models （2022.10.07）
👉Complete paper list 🔗 for "Chain of Thought"👈
Language Model Crossover: Variation through Few-Shot Prompting （2023.02.23）
Evaluating the Robustness of Discrete Prompts （2023.02.11）
PLACES: Prompting Language Models for Social Conversation Synthesis （2023.02.07）
Large Language Models Can Be Easily Distracted by Irrelevant Context （2023.01.31）
Emergent Analogical Reasoning in Large Language Models （2022.12.19）
Discovering Language Model Behaviors with Model-Written Evaluations （2022.12.19）
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning （2022.12.15）
Solving math word problems with process- and outcome-based feedback （2022.11.25）
Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks （2022.11.22）
Large Language Models with Controllable Working Memory （2022.11.09）
👉Complete paper list 🔗 for "Evaluation & Reliability"👈
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks （2023.03.01）
Language Model Crossover: Variation through Few-Shot Prompting （2023.02.23）
How Does In-Context Learning Help Prompt Tuning? （2023.02.22）
Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints （2023.02.17）
One Embedder, Any Task: Instruction-Finetuned Text Embeddings （2022.12.19）
Complementary Explanations for Effective In-Context Learning （2022.11.25）
Prompting GPT-3 To Be Reliable （2022.10.17）
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them （2022.10.17）
Complexity-Based Prompting for Multi-Step Reasoning （2022.10.03）
Rationale-Augmented Ensembles in Language Models （2022.07.02）
👉Complete paper list 🔗 for "In-context Learning"👈
Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models （2023.03.08）
Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning （2023.03.06）
Multimodal Chain-of-Thought Reasoning in Language Models （2023.02.02）
CoHOZ: Contrasive Multimodal prompt Tuning for Hierarchical Open-set Zero-shot Recognition （2022.10.10）
VIMA: General Robot Manipulation with Multimodal Prompts （2022.10.06）
Learning to Prompt for Vision-Language Models （2022.09.01）
Visual Prompt Tuning （2022.03.23）
Multimodal Few-Shot Learning with Frozen Language Models （2021.06.25）
Similarity-Aware Multimodal Prompt Learning for Fake News Detection
👉Complete paper list 🔗 for "Multimodal Prompt"👈
SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks （2023.03.01）
Soft Prompt Guided Joint Learning for Cross-Domain Sentiment Analysis （2023.03.01）
EvoPrompting: Language Models for Code-Level Neural Architecture Search （2023.02.28）
Grimm in Wonderland: Prompt Engineering with Midjourney to Illustrate Fairytales （2023.02.17）
LabelPrompt: Effective Prompt-based Learning for Relation Classification （2023.02.16）
Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition （2023.02.16）
Prompting for Multimodal Hateful Meme Classification （2023.02.08）
QaNER: Prompting Question Answering Models for Few-shot Named Entity Recognition （2022.03.03）
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting （2021.08.31）
👉Complete paper list 🔗 for "Prompt Application"👈
This repo is maintained by EgoAlpha Lab. Questions and discussions are welcome via [email protected].
We are willing to engage in discussions with friends from the academic and industrial communities, and explore the latest developments in prompt engineering and in-context learning together.
Thanks to the PhD students from EgoAlpha Lab and other workers who participated in this repo. We will improve the project in the follow-up period and maintain this community well. We also would like to express our sincere gratitude to the authors of the relevant resources. Your efforts have broadened our horizons and enabled us to perceive a more wonderful world.