Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

How to efficiently serve ever-larger trained natural language models in practice has become
exceptionally challenging even for powerful cloud servers due to their prohibitive …

When scaled to hundreds of billions of parameters, pretrained language models such as
GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance. However, enormous …

Transformer based models, like BERT and RoBERTa, have achieved state-of-the-art results
in many Natural Language Processing tasks. However, their memory footprint, inference …

Pre-training has improved model accuracy for both classification and generation tasks at the
cost of introducing much larger and slower models. Pruning methods have proven to be an …

Transformer-based pre-training models like BERT have achieved remarkable performance
in many natural language processing tasks. However, these models are both computation …

Benchmarks such as GLUE have helped drive advances in NLP by incentivizing the creation
of more accurate models. While this leaderboard paradigm has been remarkably successful …

Pre-trained Transformer-based models have achieved state-of-the-art performance for
various Natural Language Processing (NLP) tasks. However, these models often have …

The representations learned by large-scale NLP models such as BERT have been widely
used in various tasks. However, the increasing model size of the pre-trained models also …