How often is the LLMRefs database updated?

The database is regularly updated with new and relevant academic papers as they are published.

Can I submit papers to be included in LLMRefs?

The site primarily relies on automated aggregation and curation; direct user submissions for inclusion are not explicitly advertised.

Does LLMRefs provide full access to papers?

No, it typically provides summaries and links to the original source (e.g., arXiv, publisher's site) where the full paper can be accessed.

Is there a cost to use LLMRefs?

LLMRefs appears to be a free resource for the community.

LLMRefs

LLMRefs is a curated, searchable directory of academic papers, research, and resources focused on Large Language Models (LLMs). It helps researchers and developers stay updated on the latest advancements.

Price: Free

Categories:AI Search Engines AI Tools for Students Free AI Tools

Visit Tool

Description

LLMRefs serves as a centralized hub for anyone interested in the rapidly evolving field of Large Language Models. It aggregates relevant academic papers from various sources, providing summaries, links to original papers, and often categorizations by topic or technique. This tool is invaluable for AI researchers, machine learning engineers, and academics who need to quickly find, understand, and track key developments in LLM architecture, training, applications, and evaluations. It stands out as a specialized search engine for LLM research, simplifying the process of literature review and knowledge discovery in a niche but highly active domain, making it easier to navigate the vast amount of new research.

How to Use

1.Visit the LLMRefs website.

2.Use the search bar to look for papers by keywords, authors, or topics (e.g., 'RAG,' 'fine-tuning,' 'GPT-4').

3.Browse curated lists or categories for specific areas of LLM research.

4.Click on a paper's entry to view its summary and access the link to the full PDF or arXiv page.

5.Bookmark or save relevant papers for future reference.

Use Cases

Academic researchLLM developmentMachine learning engineeringStaying updated on AI advancementsLiterature reviewEducational resourceCompetitive analysis in AI

Pros & Cons