This repository serves as a comprehensive, community-driven collection of papers, resources, and insights related to Large Language Models (LLMs), directly stemming from the survey paper "A Survey of Large Language Models." It targets researchers, engineers, and practitioners in the NLP and AI fields, offering a structured overview of LLM evolution, architectures, training methodologies, adaptation techniques, and evaluation strategies.

How It Works

The project organizes information thematically, mirroring the structure of the survey paper. It meticulously catalogs LLM models (both public and closed-source), datasets, deep learning frameworks, and architectural components (e.g., attention mechanisms, normalization layers). It also details various training algorithms, adaptation tuning methods (instruction tuning, alignment tuning, parameter-efficient tuning), and utilization techniques like in-context learning and chain-of-thought reasoning.

Quick Start & Requirements

This repository is primarily a curated knowledge base. There are no direct installation or execution commands. Accessing the information involves browsing the GitHub repository and linked resources.

Highlighted Details

• Provides detailed timelines and evolutionary graphs for prominent LLM families like GPT and LLaMA.
• Includes extensive lists of commonly used corpora and deep learning libraries relevant to LLM development.
• Features in-depth discussions on advanced topics such as long context modeling, LLM-based agents, and retrieval-augmented generation.
• Offers practical insights into prompt design and empirical analyses of instruction tuning and alignment strategies.

Maintenance & Community

The project is actively maintained, with frequent updates logged in the "Update Log" section, reflecting the rapid pace of LLM research. Readers are encouraged to contribute suggestions and report errors via email.

Licensing & Compatibility

The repository content itself is generally available for informational purposes. Specific licenses for cited papers or models would need to be checked individually.

Limitations & Caveats

As a survey and resource collection, the repository does not provide executable code or models. The information is a snapshot of research up to the last update, and the rapidly evolving LLM landscape means some details may become dated.