LML DAP

LML & DAP

Language Model Learning a Dataset for Data-Augmented Prediction

An alternative of Machine Learning using LLMs

Note

Please star the repository to show your support.

Why LML & DAP:

• Classification tasks are typically handled using Machine Learning (ML) models, which lack a balance between accuracy and interpretability.
• This project introduces a new approach to using Large Language Models (LLMs) for classification tasks in an explainable way.
• Unlike ML models that rely heavily on data cleaning and feature engineering, this method streamlines the process using LLMs.

LML process:

DAP process:

Result:

Created by Praneeth Vadlapati (@prane-eth)

? Research Paper

A preprint of the research paper is available on arXiv

? Citation

To use my paper for reference, please cite it as below:

@misc{vadlapati2024lmldap,
	title={{LML-DAP: Language Model Learning a Dataset for Data-Augmented Prediction}},
	author={{Praneeth Vadlapati}},
	year={2024},
	eprint={2409.18957},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2409.18957}
}

Quick Start

pip install -r requirements.txt
cp .env.example .env

Now, edit the .env file and add your values.
Run the file Experiment-LML.ipynb

More Projects

For more projects, open the profile: @Pro-GenAI

Contributing

Contributions are welcome! Feel free to create an issue for any bug reports or suggestions.
To contribute, please star the repository and create an Issue. If I can't solve that, I will allow anyone to create a pull request.

? License

Copyright © 2024 Praneeth Vadlapati
Please refer to the LICENSE file for more information.
To request a permission to use my work, please contact me using the link below.

️ Disclaimer

The code is not intended for use in production environments. This code is for educational and research purposes only. No author is responsible for any misuse or damage caused by this code. Use it at your own risk. The code is provided as is without any guarantees or warranty.

Acknowledgements

• Special thanks to Groq (https://groq.com/) for a fast LLM inference which saved me time for this research project.
• Image Credits:
  • Database: https://pixabay.com/vectors/database-data-storage-cylinder-149760/
  • Data Summary: https://pixabay.com/vectors/documents-paper-text-icon-149799/
  • Test Data: https://pixabay.com/vectors/flask-test-tube-icon-medical-5817899/
  • Checkmark: https://pixabay.com/vectors/confirmation-symbol-icon-green-1152155/

? Contact

For personal queries, please find my contact details here: linktr.ee/prane.eth