An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab.
📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt ｜ ⛳ LLMs Usage Guide
⭐️ 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?
☄️ 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。
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[2024.5.10]
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[2024.5.9]
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[2024.5.8]
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[2024.5.7]
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[2024.5.6]
- Paper: Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models
- Paper: On the test-time zero-shot generalization of vision-language models: Do we really need prompt learning?
- Paper: Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows
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[2024.5.5]
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[2024.5.4]
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[2024.5.3]
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[2024.5.2]
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[2024.5.1]
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[2024.4.30]
- Paper: EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model
- Paper: NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance
- Paper: Navigating WebAI: Training Agents to Complete Web Tasks with Large Language Models and Reinforcement Learning
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[2024.4.29]
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[2024.4.28]
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[2024.4.27]
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[2024.4.26]
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[2024.4.25]
- Paper: Cantor: Inspiring Multimodal Chain-of-Thought of MLLM
- Paper: The PRISM Alignment Project: What Participatory, Representative and Individualised Human Feedback Reveals About the Subjective and Multicultural Alignment of Large Language Models
- Paper: MMT-Bench: A Comprehensive Multimodal Benchmark for Evaluating Large Vision-Language Models Towards Multitask AGI
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[2024.4.24]
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[2024.4.23]
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[2024.4.22]
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[2024.4.21]
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[2024.4.20]
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[2024.4.19]
You can directly click on the title to jump to the corresponding PDF link location
The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs) （2024.03.21）
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey （2024.03.21）
ChatGPT Alternative Solutions: Large Language Models Survey （2024.03.16）
MM1: Methods, Analysis&Insights from Multimodal LLM Pre-training （2024.03.14）
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey （2024.03.14）
Model Parallelism on Distributed Infrastructure: A Literature Review from Theory to LLM Case-Studies （2024.03.06）
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation （2024.03.05）
A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods （2024.03.05）
Large Language Models for Data Annotation: A Survey （2024.02.21）
A Survey on Knowledge Distillation of Large Language Models （2024.02.20）
👉Complete paper list 🔗 for "Survey"👈
DLoRA: Distributed Parameter-Efficient Fine-Tuning Solution for Large Language Model （2024.04.08）
3P-LLM: Probabilistic Path Planning using Large Language Model for Autonomous Robot Navigation （2024.03.27）
SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts （2024.03.20）
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models （2024.03.20）
Few-Shot Class Incremental Learning with Attention-Aware Self-Adaptive Prompt （2024.03.14）
Unveiling the Generalization Power of Fine-Tuned Large Language Models （2024.03.14）
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling （2024.03.11）
VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models （2024.03.10）
Localized Zeroth-Order Prompt Optimization （2024.03.05）
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models （2024.03.04）
👉Complete paper list 🔗 for "Prompt Design"👈
Visualization-of-Thought Elicits Spatial Reasoning in Large Language Models （2024.04.04）
Can Small Language Models Help Large Language Models Reason Better?: LM-Guided Chain-of-Thought （2024.04.04）
Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models （2024.03.25）
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science （2024.03.21）
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning （2024.03.12）
ERA-CoT: Improving Chain-of-Thought through Entity Relationship Analysis （2024.03.11）
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought （2024.03.08）
Chain-of-Thought Unfaithfulness as Disguised Accuracy （2024.02.22）
Chain-of-Thought Reasoning Without Prompting （2024.02.15）
👉Complete paper list 🔗 for "Chain of Thought"👈
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models （2024.03.20）
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models （2024.03.14）
NavCoT: Boosting LLM-Based Vision-and-Language Navigation via Learning Disentangled Reasoning （2024.03.12）
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal Modeling （2024.03.11）
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought （2024.03.08）
LoRA-SP: Streamlined Partial Parameter Adaptation for Resource-Efficient Fine-Tuning of Large Language Models （2024.02.28）
Securing Reliability: A Brief Overview on Enhancing In-Context Learning for Foundation Models （2024.02.27）
GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning （2024.02.26）
DiffuCOMET: Contextual Commonsense Knowledge Diffusion （2024.02.26）
Long-Context Language Modeling with Parallel Context Encoding （2024.02.26）
👉Complete paper list 🔗 for "In-context Learning"👈
Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation （2024.04.10）
Untangle the KNOT: Interweaving Conflicting Knowledge and Reasoning Skills in Large Language Models （2024.04.04）
Unveiling LLMs: The Evolution of Latent Representations in a Temporal Knowledge Graph （2024.04.04）
Retrieval-Augmented Generation for AI-Generated Content: A Survey （2024.02.29）
VerifiNER: Verification-augmented NER via Knowledge-grounded Reasoning with Large Language Models （2024.02.28）
LLM Augmented LLMs: Expanding Capabilities through Composition （2024.01.04）
ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems （2023.11.16）
Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models （2023.11.15）
From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL （2023.11.11）
Optimizing Retrieval-augmented Reader Models via Token Elimination （2023.10.20）
👉Complete paper list 🔗 for "Retrieval Augmented Generation"👈
Evaluating LLMs at Detecting Errors in LLM Responses （2024.04.04）
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers （2024.04.04）
Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models （2024.03.29）
ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models （2024.03.08）
Benchmarking the Text-to-SQL Capability of Large Language Models: A Comprehensive Evaluation （2024.03.05）
Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation （2024.03.04）
A Cognitive Evaluation Benchmark of Image Reasoning and Description for Large Vision Language Models （2024.02.28）
Evaluating Very Long-Term Conversational Memory of LLM Agents （2024.02.27）
Semantic Mirror Jailbreak: Genetic Algorithm Based Jailbreak Prompts Against Open-source LLMs （2024.02.21）
TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization （2024.02.20）
👉Complete paper list 🔗 for "Evaluation & Reliability"👈
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments （2024.04.11）
ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models （2024.04.11）
Laser Learning Environment: A new environment for coordination-critical multi-agent tasks （2024.04.04）
AutoWebGLM: Bootstrap And Reinforce A Large Language Model-based Web Navigating Agent （2024.04.04）
MIMIR: A Streamlined Platform for Personalized Agent Tuning in Domain Expertise （2024.04.03）
ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models （2024.03.29）
Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis （2024.03.28）
Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy （2024.03.25）
AIOS: LLM Agent Operating System （2024.03.25）
EduAgent: Generative Student Agents in Learning （2024.03.23）
👉Complete paper list 🔗 for "Agent"👈
BRAVE: Broadening the visual encoding of vision-language models （2024.04.10）
ORacle: Large Vision-Language Models for Knowledge-Guided Holistic OR Domain Modeling （2024.04.10）
MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation （2024.04.08）
Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs （2024.04.08）
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens （2024.04.04）
ViTamin: Designing Scalable Vision Models in the Vision-Language Era （2024.04.02）
Segment Any 3D Object with Language （2024.04.02）
Iterated Learning Improves Compositionality in Large Vision-Language Models （2024.04.02）
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models （2024.03.27）
Visual CoT: Unleashing Chain-of-Thought Reasoning in Multi-Modal Language Models （2024.03.25）
👉Complete paper list 🔗 for "Multimodal Prompt"👈
Manipulating Large Language Models to Increase Product Visibility （2024.04.11）
Generating consistent PDDL domains with Large Language Models （2024.04.11）
High-Dimension Human Value Representation in Large Language Models （2024.04.11）
MetaCheckGPT -- A Multi-task Hallucination Detector Using LLM Uncertainty and Meta-models （2024.04.10）
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications （2024.04.10）
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding （2024.04.08）
Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models （2024.04.08）
Topic-based Watermarks for LLM-Generated Text （2024.04.02）
Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks （2024.04.02）
Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference （2024.03.29）
👉Complete paper list 🔗 for "Prompt Application"👈
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models （2024.04.11）
OpenBias: Open-set Bias Detection in Text-to-Image Generative Models （2024.04.11）
Scaling Up Video Summarization Pretraining with Large Language Models （2024.04.04）
Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners （2024.04.02）
MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning （2024.03.29）
ReALM: Reference Resolution As Language Modeling （2024.03.29）
RSMamba: Remote Sensing Image Classification with State Space Model （2024.03.28）
DreamLIP: Language-Image Pre-training with Long Captions （2024.03.25）
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model （2024.03.20）
VideoMamba: State Space Model for Efficient Video Understanding （2024.03.11）
👉Complete paper list 🔗 for "Foundation Models"👈
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.
This repo is maintained by EgoAlpha Lab. Questions and discussions are welcome via helloegoalpha@gmail.com.
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.
