When Stars Align with AI: Training LLM for Astronomy Texts

Large language models like GPT-3 and PaLM have demonstrated impressive performance on many natural language tasks. However, their versatility comes at the cost of specialization – these generalist models often struggle with niche domains like academic astronomy.

To address this gap, researchers have developed AstroLLaMA, a new 7-billion parameter foundation model fine-tuned specifically on astronomical literature. AstroLLaMA was adapted from the LLaMA-2 model developed by Meta AI, using a dataset of over 300,000 abstracts from astrophysics papers on arXiv.

Learning curve of AstroLLaMA during itsfine-tuning on the arXiv astrophysics dataset.

After fine-tuning, AstroLLaMA achieved a 30% lower perplexity score compared to the original LLaMA-2, indicating significantly improved ability to predict appropriate words and phrases in astronomical texts.

Tests showed AstroLLaMA generates more relevant and nuanced continuations when prompted with paper abstracts, compared to LLaMA-2 and even the more advanced GPT-4. It exhibits competent understanding of astronomical concepts, unlike the generic responses of generalist models.

Additionally, AstroLLaMA’s text embeddings better capture the semantic similarities between papers, enabling more meaningful analysis and search in astronomy literature.

Distribution of pairwise cosine similari-ties among 10,000 randomly selected abstracts from our corpus, divided into 10 equal bins based on similarity levels from GPT-3

The researchers note AstroLLaMA still suffers some knowledge gaps that additional training could address. But its specialized design shows the value of tailoring foundation models to particular academic domains.

With further refinement, AstroLLaMA may assist astronomers via applications like summarizing papers, conversational question answering, and hypothesis generation. By focusing large models on a niche discipline, researchers can realize more robust and insightful AI capabilities.

The public release of AstroLLaMA provides a springboard for the astronomy community to build upon. Specialized foundation models like it promise to overcome the limitations of broad generalist models in scholarly fields.

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