Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

Transformers have achieved great success in many artificial intelligence fields, such as
natural language processing, computer vision, and audio processing. Therefore, it is natural …

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 …

In this report, we introduce the Gemini 1.5 family of models, representing the next generation
of highly compute-efficient multimodal models capable of recalling and reasoning over fine …

Large language models have been shown to achieve remarkable performance across a
variety of natural language tasks using few-shot learning, which drastically reduces the …

Scaling up language models has been shown to predictably improve performance and
sample efficiency on a wide range of downstream tasks. This paper instead discusses an …

Exploring large-scale pretrained foundation models is of significant interest in computer
vision because these models can be quickly transferred to many downstream tasks. This …

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 …

This paper explores a simple method for improving the zero-shot learning abilities of
language models. We show that instruction tuning--finetuning language models on a …

The past 3 years of work in NLP have been characterized by the development and
deployment of ever larger language models, especially for English. BERT, its variants, GPT …