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Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.

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An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.

📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt⛳ LLMs Usage Guide

version Awesome

⭐️ 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, Prompt Engineering, Agent, and Foundation Models.

🎉Playground🎉: Large language models(LLMs)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.

🎉LLMs Usage Guide🎉: The method for quickly getting started with large language models by using LangChain.

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 AIGC;
  • Those whose jobs are replaced by AI automation.

💎EgoAlpha: Hello! human👤, are you ready?

Table of Contents

📢 News

☄️ EgoAlpha releases the TrustGPT focuses on reasoning. Trust the GPT with the strongest reasoning abilities for authentic and reliable answers. You can click here or visit the Playgrounds directly to experience it。

👉 Complete history news 👈


📜 Papers

You can directly click on the title to jump to the corresponding PDF link location

Survey

Motion meets Attention: Video Motion Prompts2024.07.03

Towards a Personal Health Large Language Model2024.06.10

Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning2024.06.10

Towards Lifelong Learning of Large Language Models: A Survey2024.06.10

Towards Semantic Equivalence of Tokenization in Multimodal LLM2024.06.07

LLMs Meet Multimodal Generation and Editing: A Survey2024.05.29

Tool Learning with Large Language Models: A Survey2024.05.28

When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models2024.05.16

Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach2024.04.24

A Survey on the Memory Mechanism of Large Language Model based Agents2024.04.21

👉Complete paper list 🔗 for "Survey"👈

Prompt Engineering

Prompt Design

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy2024.06.28

Dataset Size Recovery from LoRA Weights2024.06.27

Dual-Phase Accelerated Prompt Optimization2024.06.19

From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries2024.06.18

VoCo-LLaMA: Towards Vision Compression with Large Language Models2024.06.18

LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation2024.06.18

The Impact of Initialization on LoRA Finetuning Dynamics2024.06.12

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models2024.06.07

Cross-Context Backdoor Attacks against Graph Prompt Learning2024.05.28

Yuan 2.0-M32: Mixture of Experts with Attention Router2024.05.28

👉Complete paper list 🔗 for "Prompt Design"👈

Chain of Thought

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models2024.06.07

Cantor: Inspiring Multimodal Chain-of-Thought of MLLM2024.04.24

nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States2024.04.04

Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models2024.04.04

Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought2024.04.04

Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models2024.03.25

A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science2024.03.21

NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning2024.03.12

ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis2024.03.11

Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought2024.03.08

👉Complete paper list 🔗 for "Chain of Thought"👈

In-context Learning

LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation2024.06.18

The Impact of Initialization on LoRA Finetuning Dynamics2024.06.12

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models2024.06.07

Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning2024.06.04

Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks2024.06.04

Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models2024.05.28

Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion2024.05.19

MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning2024.05.19

Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning2024.04.25

Stronger Random Baselines for In-Context Learning2024.04.19

👉Complete paper list 🔗 for "In-context Learning"👈

Retrieval Augmented Generation

Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning2024.06.24

Enhancing RAG Systems: A Survey of Optimization Strategies for Performance and Scalability2024.06.04

Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training2024.05.31

Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection2024.05.25

DocReLM: Mastering Document Retrieval with Language Model2024.05.19

UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models2024.05.16

ChatHuman: Language-driven 3D Human Understanding with Retrieval-Augmented Tool Reasoning2024.05.07

REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs2024.05.03

Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation2024.04.10

Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models2024.04.04

👉Complete paper list 🔗 for "Retrieval Augmented Generation"👈

Evaluation & Reliability

CELLO: Causal Evaluation of Large Vision-Language Models2024.06.27

PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation2024.06.26

Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models2024.06.24

OR-Bench: An Over-Refusal Benchmark for Large Language Models2024.05.31

TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models2024.05.28

Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models2024.05.23

HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models2024.05.16

Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark2024.05.10

Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models2024.05.03

Causal Evaluation of Language Models2024.05.01

👉Complete paper list 🔗 for "Evaluation & Reliability"👈

Agent

Cooperative Multi-Agent Deep Reinforcement Learning Methods for UAV-aided Mobile Edge Computing Networks2024.07.03

Symbolic Learning Enables Self-Evolving Agents2024.06.26

Adversarial Attacks on Multimodal Agents2024.06.18

DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning2024.06.14

Transforming Wearable Data into Health Insights using Large Language Model Agents2024.06.10

Neuromorphic dreaming: A pathway to efficient learning in artificial agents2024.05.24

Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning2024.05.16

Learning Multi-Agent Communication from Graph Modeling Perspective2024.05.14

Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning2024.05.09

Unveiling Disparities in Web Task Handling Between Human and Web Agent2024.05.07

👉Complete paper list 🔗 for "Agent"👈

Multimodal Prompt

InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output2024.07.03

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy2024.06.28

Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs2024.06.28

LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context Compression2024.06.28

Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs2024.06.24

VoCo-LLaMA: Towards Vision Compression with Large Language Models2024.06.18

Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models2024.06.12

An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models2024.06.07

Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning2024.06.04

DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models2024.05.31

👉Complete paper list 🔗 for "Multimodal Prompt"👈

Prompt Application

IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization2024.07.03

Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs2024.06.28

OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding2024.06.27

Adversarial Search Engine Optimization for Large Language Models2024.06.26

VideoLLM-online: Online Video Large Language Model for Streaming Video2024.06.17

Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs2024.06.14

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation2024.06.10

Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences2024.06.07

PaCE: Parsimonious Concept Engineering for Large Language Models2024.06.06

Yuan 2.0-M32: Mixture of Experts with Attention Router2024.05.28

👉Complete paper list 🔗 for "Prompt Application"👈

Foundation Models

TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts2024.07.03

Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning2024.07.01

Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs2024.06.28

OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding2024.06.27

Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs?2024.06.27

Efficient World Models with Context-Aware Tokenization2024.06.27

The Remarkable Robustness of LLMs: Stages of Inference?2024.06.27

ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models2024.06.26

AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation2024.06.18

Unveiling Encoder-Free Vision-Language Models2024.06.17

👉Complete paper list 🔗 for "Foundation Models"👈

👨‍💻 LLM Usage

Large language models (LLMs) are becoming a revolutionary technology that is shaping the development of our era. Developers can create applications that were previously only possible in our imaginations by building LLMs. However, using these LLMs often comes with certain technical barriers, and even at the introductory stage, people may be intimidated by cutting-edge technology: Do you have any questions like the following?

  • How can LLM be built using programming?
  • How can it be used and deployed in your own programs?

💡 If there was a tutorial that could be accessible to all audiences, not just computer science professionals, it would provide detailed and comprehensive guidance to quickly get started and operate in a short amount of time, ultimately achieving the goal of being able to use LLMs flexibly and creatively to build the programs they envision. And now, just for you: the most detailed and comprehensive Langchain beginner's guide, sourced from the official langchain website but with further adjustments to the content, accompanied by the most detailed and annotated code examples, teaching code lines by line and sentence by sentence to all audiences.

Click 👉here👈 to take a quick tour of getting started with LLM.

✉️ Contact

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.

🙏 Acknowledgements

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.