Abstract

I discuss the application of Foundation Models in Astronomy through the collaborative efforts of the UniverseTBD consortium with a mission to democratize Science for everyone. One of our key objectives is to overcome the limitations of general-purpose Foundation Models, such as producing limited information in specialized fields. To this end, we have developed the first specialized large language model for Astronomy, AstroLLaMa-1. This model, enhanced by exposure to domain-specific literature from the NASA Astrophysics Data System and ArXiv, demonstrates improved text completion and embedding capabilities over existent GPT models. I further discuss the potential of LLMs in generating complex scientific hypotheses and extracting meaningful insights from astronomy literature. Our findings, validated by human experts, demonstrate the LLM capability in informed scientific critique and uncover intriguing patterns in the embedding space, highlighting the potential of LLMs to augment scientific inquiry. I will also discuss preliminary work with the multi-modal model AstroLLaVA, which allows us to interact with astronomical images via natural language. Through the work of UniverseTBD, we aim to explore how artificial intelligence can assist human intelligence in Astronomy and, more broadly, Science.

Biography

Ioana Ciucă, who goes by Jo, is an interdisciplinary Jubilee Joint Fellow at the Australian National University, working across the School of Computing and the Research School of Astronomy & Astrophysics. Before joining ANU, Jo finished her PhD in Astrophysics at University College London in the United Kingdom, where she worked at the intersection of Astronomy and Machine Learning to understand the formation and evolution history of our Galaxy, the Milky Way. Jo is now focusing on utilizing foundation models that benefit researchers everywhere, working alongside the UniverseTBD team of more than 30 astronomers, engineers, ML practitioners and enthusiasts worldwide.