RAG-arXiv-Papers

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RAG-arXiv-Papers

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Chat with the Top LLM and NLP Research Papers

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# Chat with the Top LLM and NLP Research Papers
<p align="center">
  <img src="https://github.com/akhilchibber/RAG-arXiv-Papers/blob/main/arXiv.png?raw=true" alt="earthml Logo">
</p>

## Dataset
The dataset used in this project can be found on Kaggle: [arXiv Papers Dataset](https://www.kaggle.com/datasets/harshsinghal/nlp-and-llm-related-arxiv-papers/data).

## Objective
- To provide a comprehensive collection of academic papers on Large Language Models (LLMs), ChatGPT, Transformers, and related topics.
- To facilitate quick access to seminal and cutting-edge research for enthusiasts, researchers, and practitioners in the field of artificial intelligence.
- To support the community in identifying research gaps and opportunities within the domain of Retrieval Augmented Generation (RAG) and beyond.

## Key Components
- **Curated List of Papers**: An annotated compilation of research papers, including titles, authors, summaries, and key findings.
- **Jupyter Notebook Overview**: A detailed notebook presenting the collection with insights and annotations for each paper.
- **Research Focus Areas**: Emphasis on Retrieval Augmented Generation, LLMs, ChatGPT, and Transformers among other AI technologies

## Functionality
- **Research Acceleration**: Streamlines the research process by offering an organized repository of significant works.
- **Educational Resource**: Serves as a learning tool for those new to the field or looking to expand their knowledge on specific topics within LLMs and Transformers.
<p align="center">
  <img src="https://github.com/akhilchibber/RAG-arXiv-Papers/blob/main/RAG-With-LLM.jpeg" alt="earthml Logo">
</p>

## Use Case
- **Academic and Industry Research**: Ideal for researchers and professionals seeking to deepen their understanding or contribute to the fields of LLMs, ChatGPT, and Transformers.
- **Project Inspiration**: Provides a foundation for developing new projects or studies based on the latest trends and findings in AI research.

## Getting Started
To get started with this project:

1. Clone this repository to your local machine.
2. Ensure you have Jupyter Notebook installed and running.
3. Install the required dependencies.
4. Download the "arXiv Papers Dataset" and place it in the designated directory.
5. Open and run the Jupyter Notebook "RAG_arXiv_Papers.ipynb" to train and evaluate the model.

## Contributing
We welcome contributions to enhance the functionality and efficiency of this script. Feel free to fork, modify, and make pull requests to this repository. To contribute:

1. Fork the Project.
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`).
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`).
4. Push to the Branch (`git push origin feature/AmazingFeature`).
5. Open a Pull Request against the `main` branch.

## License

This project is licensed under the MIT License - see the `LICENSE` file for details.

## Contact

Author: Akhil Chhibber

LinkedIn: https://www.linkedin.com/in/akhilchhibber/

Medium Blogs: https://medium.com/@akhil.chibber

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