This survey explores how Deep Learning has battled the COVID-19 pandemic and provides
directions for future research on COVID-19. We cover Deep Learning applications in Natural …

Transformer-based pretrained language models (T-PTLMs) have achieved great success in
almost every NLP task. The evolution of these models started with GPT and BERT. These …

Large language models (LLMs) have been shown to be able to perform new tasks based on
a few demonstrations or natural language instructions. While these capabilities have led to …

Information overload is a major obstacle to scientific progress. The explosive growth in
scientific literature and data has made it ever harder to discover useful insights in a large …

Language models pretrained on text from a wide variety of sources form the foundation of
today's NLP. In light of the success of these broad-coverage models, we investigate whether …

The utilization of large language models (LLMs) for Healthcare has generated both
excitement and concern due to their ability to effectively respond to free-text queries with …

This paper presents E5, a family of state-of-the-art text embeddings that transfer well to a
wide range of tasks. The model is trained in a contrastive manner with weak supervision …

Abstract The C ovid-19 Open Research Dataset (CORD-19) is a growing 1 resource of
scientific papers on C ovid-19 and related historical coronavirus research. CORD-19 is …

As language models grow ever larger, the need for large-scale high-quality text datasets has
never been more pressing, especially in multilingual settings. The BigScience workshop, a 1 …

We introduce INSTRUCTOR, a new method for computing text embeddings given task
instructions: every text input is embedded together with instructions explaining the use case …