Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

We introduce the Universal Speech Model (USM), a single large model that performs
automatic speech recognition (ASR) across 100+ languages. This is achieved by pre …

This paper presents contrastive-tuning, a simple method employing contrastive training to
align image and text models while still taking advantage of their pre-training. In our empirical …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Recently, the speech community is seeing a significant trend of moving from deep neural
network based hybrid modeling to end-to-end (E2E) modeling for automatic speech …

Since their introduction in 2017, transformers have quickly become the dominant
architecture for achieving state-of-the-art results on a variety of natural language processing …

Motivated by the success of masked language modeling (MLM) in pre-training natural
language processing models, we propose w2v-BERT that explores MLM for self-supervised …

This paper presents a Generative RegIon-to-Text transformer, GRiT, for object
understanding. The spirit of GRiT is to formulate object understanding as< region, text> …