LLMSurvey provides a curated collection of research papers and resources centered around Large Language Models (LLMs). This project aims to consolidate information and facilitate understanding in this rapidly evolving field. It addresses the complex challenge of navigating the vast and growing body of literature concerning LLMs, offering a structured approach to learning about their development, applications, and limitations. The project primarily leverages and organizes existing research for accessibility.
This project distinguishes itself through its comprehensive and continuously updated collection of resources, spanning papers, timelines, and lists of models. It provides specific attention to recent advancements like long Chain-of-Thought reasoning and offers practical tips for prompt engineering. Furthermore, the architecture diagrams and carefully curated lists of models offer valuable insights for researchers and practitioners alike.
- Chinese Version: Provides a translated version of the survey for a wider audience, specifically beginners in LLMs.
- arxiv Trends: Tracks the increasing number of research papers related to LLMs on arXiv, highlighting the field's rapid growth.
- Model List: Offers a detailed and up-to-date catalog of publicly available LLMs with key information like release time, size, and links to papers.
The project is actively maintained, with recent updates including new content on long CoT reasoning and a Chinese version of the survey. The GitHub repository shows consistent activity and engagement with the community through issues and pull requests. Detailed tables, lists, and the table of contents point to deliberate organization and ongoing expansion.
LLMSurvey benefits researchers, practitioners, and students seeking a consolidated overview of LLMs. It addresses the need for a readily accessible and structured resource in this dynamic field by providing curated papers, model lists, and experimental information. This information is crucial for understanding the current state of LLM research and development, enabling efficient exploration and application.