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.

翻译：大语言模型在许多人类语言任务中表现出色，但在高度专业化领域（如学术天文学）中往往表现不佳。为填补这一差距，我们提出了AstroLLaMA，这是一个基于LLaMA-2微调而成的70亿参数模型，使用了来自arXiv的超过30万篇天文学摘要。针对传统因果语言建模进行了优化，AstroLLaMA的困惑度比LLaMA-2降低了30%，展现出显著的领域适应性。尽管参数数量明显较少，但我们的模型在文本补全和嵌入提取方面比最先进的基础模型更能生成富有洞察力且具有科学相关性的内容。AstroLLaMA作为一个稳健的领域专用模型，具有广泛的微调潜力。将其公开发布旨在推动以天文学为重点的研究，包括自动论文摘要和对话代理的开发。