This repository serves as a curated collection of research paper notes and summaries focused on Large Language Models (LLMs) and related fields, targeting algorithm engineers and researchers. It aims to provide accessible insights into cutting-edge advancements in areas like multimodal AI, Parameter-Efficient Fine-Tuning (PEFT), Retrieval-Augmented Generation (RAG), and explainability.

How It Works

The repository organizes notes by topic, with each entry typically detailing a specific research paper. For each paper, it outlines the motivation, the proposed methodology, experimental results, and often includes links to the original paper and associated code repositories. This structured approach allows for a systematic understanding of individual contributions within the broader LLM landscape.

Quick Start & Requirements

• Access: No installation is required; the content is accessible directly through the GitHub repository.
• Requirements: A web browser and an internet connection are sufficient. Familiarity with LLM concepts and research paper structures is beneficial.
• Resources: Minimal computational resources are needed, primarily for browsing the content.

Highlighted Details

• Comprehensive coverage of key LLM subfields: Multimodality, PEFT, RAG, Agents, Explainability, and more.
• Detailed summaries of numerous influential papers, including methodologies, results, and links.
• Categorization by specific research areas, facilitating targeted learning.
• Inclusion of related repositories for broader context and learning resources.

Maintenance & Community

The repository is maintained by Yang Xi (km1994) and appears to be an ongoing personal project. Community engagement is encouraged through QQ groups and WeChat, as indicated by the README.

Licensing & Compatibility

The repository itself is not licensed, but it references and links to numerous open-source papers and codebases, which are governed by their respective licenses. Users should consult the licenses of linked projects for usage and distribution terms.

Limitations & Caveats

This repository is a collection of notes and summaries, not a runnable codebase. The depth and accuracy of the summaries depend on the author's interpretation. Some links may become outdated over time.