학술논문
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
Document Type
Working Paper
Author
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; UniverseTBD
Source
Subject
Language
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
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