NSF Award
2214708
Machine learning is revolutionizing many parts of society, but training the very best models requires tremendous computing resources that are often out of reach for academic groups. This project therefore acquires a special-purpose instrument, named the LanguageLens, that is designed to process vast amounts of natural language text. The LanguageLens will support research in natural language processing, deep learning, computational linguistics, crisis informatics, conversational AI, neural machine translation, and legal corpus linguistics, and will enable academic research to advance both the machine learning needed to train large models, as well as societially relevant applications of those models.<br/><br/>The LanguageLens is a high-performance GPU cluster that balances compute, storage and internode communication to support a variety of demanding NLP-based workloads. The LanguageLens will be focused on solving research projects that have the potential for transformational, interdisciplinary impact across a wide variety of fields. A key area of focus for the instrument is the ability to train new large-scale language models and to examine their inner workings in real-time. Language models will be trained with specific downstream applications in mind, on novel corpora as well as with novel neuro-symbolic architectures, to help derive insight from the resulting weights. The LanguageLens will prioritize support for research that addresses pressing societal problems. It will also provide authentic workforce training and educational experiences for students: as the resource gap between industry and academia grows, it is increasingly difficult to give them opportunities to pursue high-impact research that involves huge models and datasets. Finally, as many companies refuse to release the pretrained weights of their models, a central goal is to make trained weights freely available to everyone, subject to ethical considerations, to drive national impact for both industry and academia. Project resources such as code, publications, datasets and pretrained models will be available through the LanguageLens website at https://ll.cs.byu.edu/.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
