awesome-llm-papers.github.io
A curated collection of the most impactful papers, tools, and resources on Large Language Models (LLMs). Continuously updated to help researchers, developers, and enthusiasts stay on top of LLM advancements.
https://github.com/awesome-llm-papers/awesome-llm-papers.github.io
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (16.7%) to scientific vocabulary
Keywords
Repository
A curated collection of the most impactful papers, tools, and resources on Large Language Models (LLMs). Continuously updated to help researchers, developers, and enthusiasts stay on top of LLM advancements.
Basic Info
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Metadata Files
README.md
Awesome LLM Papers
Explore the latest LLM research with clarity.
A visual, searchable map of papers on large language models built to help researchers, engineers, and enthusiasts stay up to date.
Live site: awesome-llm-papers.github.io
Why This Exists
The field of LLMs is growing at breakneck speed.
Every week brings a wave of new papers on prompting, fine-tuning, evaluation, RAG, and more. Traditional search tools make it hard to keep track, compare, or explore emerging trends.
LLM Bible solves that by providing an interactive, topic-clustered map of LLM papers with filters, search, and direct links to arXiv.
Features
- 2D Visualization: Explore papers spatially via t-SNE clustering
- Search: Instantly filter by title, author, or key topics (e.g., "prompting", "RAG")
- Filter by year: Focus on recent or historical research
- Tag-based discovery: See papers grouped by categories like evaluation, agents, multimodal, etc.
- No login, no ads, no fluff just open research
Overview
Get Started
Visit: https://awesome-llm-papers.github.io
How It Works
This site is statically hosted using GitHub Pages and built with: - Python backend (for data parsing and clustering) - JavaScript (for interactive visualization) - HTML + CSS (for fast, lightweight performance)
All data is sourced from arXiv and processed into a local JSON format.
Contributing
Have feature ideas or want to help improve paper classification, tagging, or UI?
- Fork the repo
- Submit a pull request
- Or open an issue
Wed love to collaborate!
Support & Share
If this project helps you:
- Give it a on GitHub
- Share the live site with friends, classmates, or on Twitter/LinkedIn
- Suggest new papers or topics to track
License
This project is licensed under the GNU General Public License v3.0.
You are free to use, study, share, and modify the code but any derivative work must also be open-sourced under the GPLv3.
Commercial or proprietary redistribution is not allowed unless it complies fully with the terms of the GPL.
See the LICENSE file for full details.
Owner
- Login: awesome-llm-papers
- Kind: user
- Repositories: 1
- Profile: https://github.com/awesome-llm-papers
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