llmsurvey is an awesome-list that organizes research papers and resources related to Large Language Models. The collection is structured around an academic survey paper and includes categorized model lists, prompt design tips, and visual evolutionary graphs.
Project overview
The project systematically organizes the expanding volume of LLM literature by structuring its resource collection around a comprehensive academic survey paper, supplemented by visual model evolutionary graphs and prompt design guidance.
Project type
Infrastructure · Prompt Engineering · Learning Resources · Model Development
Use cases
Learning & Education
Deployment
Refer to project documentation
License
License pending
Best for
Researchers and educators navigating the expanding volume of LLM research papers and related resources.
Developers seeking categorized lists of LLMs, pre-training corpora, deep learning libraries, and prompt design tips.
Key capabilities
A structured and categorized collection of papers and resources related to Large Language Models, organized following a survey paper.
A collection of useful tips for designing prompts, gathered from online notes and authors' experiences.
Categorized lists of publicly available and closed-source LLMs, commonly used pre-training corpora, and relevant deep learning libraries.
Visual illustrations of the technical evolution of GPT-series models and the LLaMA family evolutionary graph.
Limitations and risks
This project is not a deployable application or software library; it is strictly a research paper and resource awesome-list.
Getting started
Reading the GitHub repository and linked external resources requires no complex setup. Navigate the README.md Table of Contents to find desired resources.
Evidence and sources
README: > A collection of papers and resources related to Large Language Models.
README: The Chinese book focuses on providing explanations for beginners in the field of LLMs, aiming to present a comprehensive framework and roadmap for LLMs. This book is suitable for…
README: A sharp increase occurs after the release of ChatGPT: the average number of published arXiv papers that contain “large language model” in title or abstract goes from 0.40 per day…
README: A brief illustration for the technical evolution of GPT-series models.
README: An evolutionary graph of the research work conducted on LLaMA.
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