Atacama Large Aperture Submillimeter Telescope (atlast) Science: Probing the Transient and Time-Variable Sky
Open research Europe(2025)
University of Pennsylvania Department of Physics and Astronomy | Texas Tech University Department of Physics & Astronomy | University of Oxford Astrophysics | Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences | Eureka Scientific | The Catholic University of America | Universi-dad Complutense de Madrid Departamento de Física de la Tierra y Astrofísica e Instituto de Física de Partículas y del Cosmos (IPARCOS) | University of Oslo Institute of Theoretical Astrophysics | Universidad Diego Portales (UDP) Instituto de Estudios Astrofísicos Facultad de Ingeniería y Ciencias | INAF -Osservatorio Astronomico di Brera | Max-Planck-Institut f ür Extraterrestrische Physik | Universit à degli Studi di Bologna INAF -Osservatorio di Astrofisica e Scienza dello Spazio di Bologna | UK Astronomy Technology Centre | IFPU -Institute for Fundamental Physics of the Universe Department of Physics | NRC Herzberg Astronomy and Astrophysics | European Southern Observatory (ESO) | NASA Goddard Space Flight Center | Universidad de La Laguna Dpto. Astrofísica | DTU-Space Cosmic Dawn Center (DAWN) | Chinese Academy of Sciences Purple Mountain Observatory | Cardiff University School of Physics & Astronomy | California Institute of Technology Division of Geological and Planetary Sciences
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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