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MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
Zhixun Chen, Ping Guo, Wenhan Han, Yifan Zhang, Binbin Liu, Haobin Lin, Fengze Liu, Yan Zhao, Bingni Zhang, Taifeng Wang, Yin Zheng, Meng Fang
https://arxiv.org/abs/2507.01785

MuRating: A High Quality Data Selecting Approach to Multilingual Large Language Model Pretraining
Data quality is a critical driver of large language model performance, yet existing model-based selection methods focus almost exclusively on English. We introduce MuRating, a scalable framework that transfers high-quality English data-quality signals into a single rater for 17 target languages. MuRating aggregates multiple English "raters" via pairwise comparisons to learn unified document-quality scores,then projects these judgments through translation to train a multilingual evaluator on mon…

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
Vladislav Esaulov, Jieyang Chen, Norbert Podhorszki, Fred Suter, Scott Klasky, Anu G Bourgeois, Lipeng Wan
https://arxiv.org/abs/2506.17084

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows
In modern science, the growing complexity of large-scale projects has increased reliance on cross-facility workflows, where institutions share resources and expertise to accelerate discovery. These workflows often involve transferring massive data over wide-area networks. While high-speed networks like ESnet and data transfer services like Globus have improved data mobility, challenges remain. Large data volumes can strain bandwidth, TCP suffers from retransmissions due to packet loss, and trad…