# AstroMLab

AstroMLab is a diverse group of researchers dedicated to advancing the application of Large Language Models (LLMs) in astronomy. Our team includes:
- Leading astronomers, astrophysicists, and cosmologists.
- Natural language processing experts.
- Frontier arXivists from the NASA Astrophysics Data System

## Objectives
- Develop specialized LLMs for astronomy
- Create open-source models for advanced research
- Facilitate LLM-driven end-to-end agentic research in astronomy

## Current Work

Our ongoing projects include:

- Curation of an astronomy-based benchmarking dataset
- Development of specialized astronomy LLMs
- Performance evaluation of models on astronomical tasks

## Models and Performance

We have developed several models, including AstroSage-LLaMA-3.1-8B ([de Haan et al. 2024](https://arxiv.org/abs/2411.09012)), AstroLLaMA-2-70B ([Pan et al. 2024](https://arxiv.org/abs/2409.19750)), and AstroLLaMA-3-8B ([Pan et al. 2024](https://arxiv.org/abs/2409.19750)). Our AstroSage-LLaMA-3.1-8B model has demonstrated strong performance in astronomy Q&A tasks ([Ting et al. 2024](https://arxiv.org/abs/2407.11194)):

| Model | Score (%) |
|-------|-----------|
| **AstroSage-LLaMA-3.1-8B (AstroMLab)** | **80.9** |
| LLaMA-3.1-8B | 73.7 |
| Phi-3.5-4B | 72.8 |
| Gemma-2-9B | 71.5 |
| LLaMA-2-70B | 70.7 |
| Qwen-2.5-7B | 70.4 |
| Yi-1.5-9B | 68.4 |
| InternLM-2.5-7B | 64.5 |
| Mistral-7B-v0.3 | 63.9 |
| ChatGLM3-6B | 50.4 |
| AstroLLaMA-2-7B (UniverseTBD) | 44.3 |

AstroSage-LLaMA-3.1-8B ([de Haan et al. 2024](https://arxiv.org/abs/2411.09012)), our lightweight model, currently achieves the highest score among the ~8B parameter models in its astronomy knowledge recall ability.

![Cost and performance trade-off in astronomical Q&A](https://cdn-uploads.huggingface.co/production/uploads/643f1ddce2ea47d170103537/ip0Bk-LZRrCArimets4H7.png)

## Support and Resources

Our research benefits from:
- Access to the Frontier nodes at Oak Ridge Leadership Computing Facility
- Support from Microsoft's Accelerating Foundation Models Research (AFMR) program

## Contact

For inquiries or collaboration opportunities, please contact: astromachinelearninglab@gmail.com