AstroLLaMA: Towards Specialized Foundation Models in Astronomy
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.
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Tuan Dung Nguyen
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Yuan-Sen Ting
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Maja Jabłońska
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Ernest Perkowski
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Jason Li
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Tomasz Różański
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Pranav Khetarpal
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Sharaf Zaman
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David Brodrick
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David Brodrick
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Sergio J. Rodríguez Méndez
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Thang Bui
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Jill Naiman
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Jesse Cranney
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Jesse Cranney
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Kevin Schawinski
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UniverseTBD
AstroLLaMA: Towards Specialized Foundation Models in Astronomy
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.
AstroLLaMA, a fine-tuned large language model using astronomy abstracts, significantly reduces perplexity and improves text completions and embeddings in specialized astronomical domains.
AstroLLaMA LLaMA-2 causal language modeling perplexity embedding extraction automatic paper summarization conversational agent development