5 papers · 1 filter
The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models
Sogol Sanjaripour, Michael J. Smith, Manuel Pérez-Carrasco +3
Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four to…
AstroLLaVA: towards the unification of astronomical data and natural language
Sharaf Zaman, Michael J. Smith, Pranav Khetarpal +8
We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue. By fine-tuning the LLaVA model on a divers…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill +27
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We pr…
AstroPT: Scaling Large Observation Models for Astronomy
Michael J. Smith, Ryan J. Roberts, Eirini Angeloudi +1
This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 mi…