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# AstroMLab |
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AstroMLab is a diverse group of researchers dedicated to advancing the application of Large Language Models (LLMs) in astronomy. Our team includes: |
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- Leading astronomers, astrophysicists, and cosmologists. |
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- Natural language processing experts. |
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- Frontier arXivists from the NASA Astrophysics Data System |
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## Objectives |
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- Develop specialized LLMs for astronomy |
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- Create open-source models for advanced research |
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- Facilitate LLM-driven end-to-end agentic research in astronomy |
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## Current Work |
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Our ongoing projects include: |
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- Curation of an astronomy-based benchmarking dataset |
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- Development of specialized astronomy LLMs |
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- Performance evaluation of models on astronomical tasks |
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## Models and Performance |
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We have developed several models, including AstroSage-8B, AstroLLaMA-2-70B, and AstroLLaMA-3-8B. Our AstroSage-8B model has demonstrated strong performance in astronomy Q&A tasks ([Ting et al. 2024](https://arxiv.org/abs/2407.11194), Pan et al. 2024): |
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| Model | Score (%) | |
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| **AstroSage-8B (AstroMLab)** | **77.2** | |
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| LLaMA-3.1-8B | 73.7 | |
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| **AstroLLaMA-2-70B (AstroMLab) | **72.3** | |
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| Gemma-2-9B | 71.5 | |
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| Qwen-2.5-7B | 70.4 | |
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| Yi-1.5-9B | 68.4 | |
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| InternLM-2.5-7B | 64.0 | |
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| Mistral-7B-v0.3 | 63.9 | |
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| ChatGLM3-6B | 50.4 | |
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| AstroLLaMA-2-7B (UniverseTBD) | 44.3 | |
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AstroSage-8B, our lightweight model, currently achieves the highest score among the ~7B parameter models in its astronomy knowledge recall ability. |
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![Cost and performance trade-off in astronomical Q&A](AstroBench.png) |
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## Support and Resources |
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Our research benefits from: |
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- Access to the Frontier nodes at Oak Ridge Leadership Computing Facility |
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- Support from Microsoft's Accelerating Foundation Models Research (AFMR) program |
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## Contact |
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For inquiries or collaboration opportunities, please contact: [email protected] |