An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.
๐ Papers | โก๏ธ Playground | ๐ Prompt Engineering | ๐ ChatGPT Prompt ๏ฝ โณ LLMs Usage Guide
โญ๏ธ Shining โญ๏ธ: This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, letโs 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:
๐Papers๐: The latest papers about In-Context Learning, Prompt Engineering, Agent, and Foundation Models.
๐Playground๐: Large language models๏ผLLMs๏ผthat enable prompt experimentation.
๐Prompt Engineering๐: Prompt techniques for leveraging large language models.
๐ChatGPT Prompt๐: Prompt examples that can be applied in our work and daily lives.
๐LLMs Usage Guide๐: 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.
๐EgoAlpha: Hello! human๐ค, are you ready?
โ๏ธ 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 itใ
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[2024.11.17]
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[2024.11.16]
- Paper:On the Surprising Effectiveness of Attention Transfer for Vision TransformersใNeurIPS2024ใ
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[2024.11.15]
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[2024.11.14]
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[2024.11.13]
- Paper:LLMStinger: Jailbreaking LLMs using RL fine-tuned LLMsใAAAI2025ใ
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[2024.11.12]
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[2024.11.11]
๐ Complete history news ๐
You can directly click on the title to jump to the corresponding PDF link location
Motion meets Attention: Video Motion Prompts ๏ผ2024.07.03๏ผ
Towards a Personal Health Large Language Model ๏ผ2024.06.10๏ผ
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning ๏ผ2024.06.10๏ผ
Towards Lifelong Learning of Large Language Models: A Survey ๏ผ2024.06.10๏ผ
Towards Semantic Equivalence of Tokenization in Multimodal LLM ๏ผ2024.06.07๏ผ
LLMs Meet Multimodal Generation and Editing: A Survey ๏ผ2024.05.29๏ผ
Tool Learning with Large Language Models: A Survey ๏ผ2024.05.28๏ผ
When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Models ๏ผ2024.05.16๏ผ
Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach ๏ผ2024.04.24๏ผ
A Survey on the Memory Mechanism of Large Language Model based Agents ๏ผ2024.04.21๏ผ
๐Complete paper list ๐ for "Survey"๐
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy ๏ผ2024.06.28๏ผ
Dataset Size Recovery from LoRA Weights ๏ผ2024.06.27๏ผ
Dual-Phase Accelerated Prompt Optimization ๏ผ2024.06.19๏ผ
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries ๏ผ2024.06.18๏ผ
VoCo-LLaMA: Towards Vision Compression with Large Language Models ๏ผ2024.06.18๏ผ
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation ๏ผ2024.06.18๏ผ
The Impact of Initialization on LoRA Finetuning Dynamics ๏ผ2024.06.12๏ผ
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models ๏ผ2024.06.07๏ผ
Cross-Context Backdoor Attacks against Graph Prompt Learning ๏ผ2024.05.28๏ผ
Yuan 2.0-M32: Mixture of Experts with Attention Router ๏ผ2024.05.28๏ผ
๐Complete paper list ๐ for "Prompt Design"๐
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models ๏ผ2024.06.07๏ผ
Cantor: Inspiring Multimodal Chain-of-Thought of MLLM ๏ผ2024.04.24๏ผ
nicolay-r at SemEval-2024 Task 3: Using Flan-T5 for Reasoning Emotion Cause in Conversations with Chain-of-Thought on Emotion States ๏ผ2024.04.04๏ผ
Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models ๏ผ2024.04.04๏ผ
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought ๏ผ2024.04.04๏ผ
Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models ๏ผ2024.03.25๏ผ
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science ๏ผ2024.03.21๏ผ
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning ๏ผ2024.03.12๏ผ
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis ๏ผ2024.03.11๏ผ
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought ๏ผ2024.03.08๏ผ
๐Complete paper list ๐ for "Chain of Thought"๐
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation ๏ผ2024.06.18๏ผ
The Impact of Initialization on LoRA Finetuning Dynamics ๏ผ2024.06.12๏ผ
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models ๏ผ2024.06.07๏ผ
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning ๏ผ2024.06.04๏ผ
Learning to grok: Emergence of in-context learning and skill composition in modular arithmetic tasks ๏ผ2024.06.04๏ผ
Long Context is Not Long at All: A Prospector of Long-Dependency Data for Large Language Models ๏ผ2024.05.28๏ผ
Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion ๏ผ2024.05.19๏ผ
MAML-en-LLM: Model Agnostic Meta-Training of LLMs for Improved In-Context Learning ๏ผ2024.05.19๏ผ
Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning ๏ผ2024.04.25๏ผ
Stronger Random Baselines for In-Context Learning ๏ผ2024.04.19๏ผ
๐Complete paper list ๐ for "In-context Learning"๐
Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning ๏ผ2024.06.24๏ผ
Enhancing RAG Systems: A Survey of Optimization Strategies for Performance and Scalability ๏ผ2024.06.04๏ผ
Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training ๏ผ2024.05.31๏ผ
Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection ๏ผ2024.05.25๏ผ
DocReLM: Mastering Document Retrieval with Language Model ๏ผ2024.05.19๏ผ
UniRAG: Universal Retrieval Augmentation for Multi-Modal Large Language Models ๏ผ2024.05.16๏ผ
ChatHuman: Language-driven 3D Human Understanding with Retrieval-Augmented Tool Reasoning ๏ผ2024.05.07๏ผ
REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs ๏ผ2024.05.03๏ผ
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation ๏ผ2024.04.10๏ผ
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models ๏ผ2024.04.04๏ผ
๐Complete paper list ๐ for "Retrieval Augmented Generation"๐
CELLO: Causal Evaluation of Large Vision-Language Models ๏ผ2024.06.27๏ผ
PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation ๏ผ2024.06.26๏ผ
Revisiting Referring Expression Comprehension Evaluation in the Era of Large Multimodal Models ๏ผ2024.06.24๏ผ
OR-Bench: An Over-Refusal Benchmark for Large Language Models ๏ผ2024.05.31๏ผ
TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models ๏ผ2024.05.28๏ผ
Subtle Biases Need Subtler Measures: Dual Metrics for Evaluating Representative and Affinity Bias in Large Language Models ๏ผ2024.05.23๏ผ
HW-GPT-Bench: Hardware-Aware Architecture Benchmark for Language Models ๏ผ2024.05.16๏ผ
Multimodal LLMs Struggle with Basic Visual Network Analysis: a VNA Benchmark ๏ผ2024.05.10๏ผ
Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models ๏ผ2024.05.03๏ผ
Causal Evaluation of Language Models ๏ผ2024.05.01๏ผ
๐Complete paper list ๐ for "Evaluation & Reliability"๐
Cooperative Multi-Agent Deep Reinforcement Learning Methods for UAV-aided Mobile Edge Computing Networks ๏ผ2024.07.03๏ผ
Symbolic Learning Enables Self-Evolving Agents ๏ผ2024.06.26๏ผ
Adversarial Attacks on Multimodal Agents ๏ผ2024.06.18๏ผ
DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning ๏ผ2024.06.14๏ผ
Transforming Wearable Data into Health Insights using Large Language Model Agents ๏ผ2024.06.10๏ผ
Neuromorphic dreaming: A pathway to efficient learning in artificial agents ๏ผ2024.05.24๏ผ
Fine-Tuning Large Vision-Language Models as Decision-Making Agents via Reinforcement Learning ๏ผ2024.05.16๏ผ
Learning Multi-Agent Communication from Graph Modeling Perspective ๏ผ2024.05.14๏ผ
Smurfs: Leveraging Multiple Proficiency Agents with Context-Efficiency for Tool Planning ๏ผ2024.05.09๏ผ
Unveiling Disparities in Web Task Handling Between Human and Web Agent ๏ผ2024.05.07๏ผ
๐Complete paper list ๐ for "Agent"๐
InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output ๏ผ2024.07.03๏ผ
LLaRA: Supercharging Robot Learning Data for Vision-Language Policy ๏ผ2024.06.28๏ผ
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs ๏ผ2024.06.28๏ผ
LLaVolta: Efficient Multi-modal Models via Stage-wise Visual Context Compression ๏ผ2024.06.28๏ผ
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs ๏ผ2024.06.24๏ผ
VoCo-LLaMA: Towards Vision Compression with Large Language Models ๏ผ2024.06.18๏ผ
Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models ๏ผ2024.06.12๏ผ
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models ๏ผ2024.06.07๏ผ
Leveraging Visual Tokens for Extended Text Contexts in Multi-Modal Learning ๏ผ2024.06.04๏ผ
DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models ๏ผ2024.05.31๏ผ
๐Complete paper list ๐ for "Multimodal Prompt"๐
IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization ๏ผ2024.07.03๏ผ
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs ๏ผ2024.06.28๏ผ
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding ๏ผ2024.06.27๏ผ
Adversarial Search Engine Optimization for Large Language Models ๏ผ2024.06.26๏ผ
VideoLLM-online: Online Video Large Language Model for Streaming Video ๏ผ2024.06.17๏ผ
Regularizing Hidden States Enables Learning Generalizable Reward Model for LLMs ๏ผ2024.06.14๏ผ
Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation ๏ผ2024.06.10๏ผ
Language models emulate certain cognitive profiles: An investigation of how predictability measures interact with individual differences ๏ผ2024.06.07๏ผ
PaCE: Parsimonious Concept Engineering for Large Language Models ๏ผ2024.06.06๏ผ
Yuan 2.0-M32: Mixture of Experts with Attention Router ๏ผ2024.05.28๏ผ
๐Complete paper list ๐ for "Prompt Application"๐
TheoremLlama: Transforming General-Purpose LLMs into Lean4 Experts ๏ผ2024.07.03๏ผ
Pedestrian 3D Shape Understanding for Person Re-Identification via Multi-View Learning ๏ผ2024.07.01๏ผ
Token Erasure as a Footprint of Implicit Vocabulary Items in LLMs ๏ผ2024.06.28๏ผ
OMG-LLaVA: Bridging Image-level, Object-level, Pixel-level Reasoning and Understanding ๏ผ2024.06.27๏ผ
Fundamental Problems With Model Editing: How Should Rational Belief Revision Work in LLMs? ๏ผ2024.06.27๏ผ
Efficient World Models with Context-Aware Tokenization ๏ผ2024.06.27๏ผ
The Remarkable Robustness of LLMs: Stages of Inference? ๏ผ2024.06.27๏ผ
ResumeAtlas: Revisiting Resume Classification with Large-Scale Datasets and Large Language Models ๏ผ2024.06.26๏ผ
AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation ๏ผ2024.06.18๏ผ
Unveiling Encoder-Free Vision-Language Models ๏ผ2024.06.17๏ผ
๐Complete paper list ๐ for "Foundation Models"๐
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?
- โ How can LLM be built using programming?
- โ How can it be used and deployed in your own programs?
๐ก 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 ๐here๐ to take a quick tour of getting started with LLM.
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.