1. AstroLLaMA: Towards Specialized Foundation Models in Astronomy
- Author
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Nguyen, Tuan Dung, Ting, Yuan-Sen, Ciucă, Ioana, O'Neill, Charlie, Sun, Ze-Chang, Jabłońska, Maja, Kruk, Sandor, Perkowski, Ernest, Miller, Jack, Li, Jason, Peek, Josh, Iyer, Kartheik, Różański, Tomasz, Khetarpal, Pranav, Zaman, Sharaf, Brodrick, David, Méndez, Sergio J. Rodríguez, Bui, Thang, Goodman, Alyssa, Accomazzi, Alberto, Naiman, Jill, Cranney, Jesse, Schawinski, Kevin, and UniverseTBD
- Subjects
Astrophysics - Instrumentation and Methods for Astrophysics ,Astrophysics - Cosmology and Nongalactic Astrophysics ,Astrophysics - Astrophysics of Galaxies ,Astrophysics - High Energy Astrophysical Phenomena ,Computer Science - Computation and Language ,Computer Science - Machine Learning - Abstract
Large language models excel in many human-language tasks but often falter in highly specialized domains like scholarly astronomy. To bridge this gap, we introduce AstroLLaMA, a 7-billion-parameter model fine-tuned from LLaMA-2 using over 300,000 astronomy abstracts from arXiv. Optimized for traditional causal language modeling, AstroLLaMA achieves a 30% lower perplexity than Llama-2, showing marked domain adaptation. Our model generates more insightful and scientifically relevant text completions and embedding extraction than state-of-the-arts foundation models despite having significantly fewer parameters. AstroLLaMA serves as a robust, domain-specific model with broad fine-tuning potential. Its public release aims to spur astronomy-focused research, including automatic paper summarization and conversational agent development., Comment: 6 pages, 3 figures, submitted to IJCNLP-AACL 2023. Comments are welcome. The model can be found on Hugging Face - https://huggingface.co/universeTBD/astrollama
- Published
- 2023