Bio: In recent years, AI, notably through advancements in Large Language Models (LLMs) such as GPT, has garnered significant attention both within academia and the broader public sphere. However, these general-purpose LLMs have been criticized for their tendency to produce spurious or ‘hallucinated’ information when grappling with specialized or technical domains.
To address this limitation, we present the first astronomy-centric LLM, AstroLLaMa, that can produce text completion and embedding that outperform GPT models. We also show that LLMs can generate scientific hypotheses of a complexity comparable to those produced by human experts through techniques such as in-context prompting and fine-tuning on domain-specific literature. Our mission is to democratize the field of astronomy by developing public-facing, AI-driven large language model tools specialized for this discipline. We posit that these specialized foundational models can revolutionize the methods we employ for literature searches and the tracing of intellectual developments within the field. We argue that the domain of physical sciences, particularly astronomy, serves as an ideal test bed for investigating the potential of modern LLMs. This inquiry stands to fundamentally reshape our understanding of both artificial and human intelligence and the boundaries of accumulated knowledge.
Bio: Yuan-Sen is an Associate Professor in astronomy and computer science at the Australian National University and an Associate Professor in astronomy at the Ohio State University. Yuan-Sen’s research applies machine learning to advance statistical inferences using large astronomical survey data. A Malaysian native, Yuan-Sen received his PhD in astronomy and astrophysics from Harvard University. After graduating, Yuan Sen was awarded a unique four-way joint postdoctoral fellowship from Princeton University, Carnegie Institute for Sciences, NASA Hubble and the Institute for Advanced Study at Princeton before reallocating to Australia.