This monograph surveys vision-language pre-training (VLP) methods for multimodal
intelligence that have been developed in the last few years. We group these approaches …

Abstract Evaluation practices in natural language generation (NLG) have many known flaws,
but improved evaluation approaches are rarely widely adopted. This issue has become …

Finetuning language models on a collection of datasets phrased as instructions has been
shown to improve model performance and generalization to unseen tasks. In this paper we …

Abstract We present the Pathways [1] Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich synthesis involving …

Effective scaling and a flexible task interface enable large language models to excel at many
tasks. We present PaLI (Pathways Language and Image model), a model that extends this …

We present Imagen, a text-to-image diffusion model with an unprecedented degree of
photorealism and a deep level of language understanding. Imagen builds on the power of …

We introduce EgoSchema, a very long-form video question-answering dataset, and
benchmark to evaluate long video understanding capabilities of modern vision and …

In the last year alone, a surge of new benchmarks to measure $\textit {compositional} $
understanding of vision-language models have permeated the machine learning ecosystem …

Large language models (LLMs) represent a major advance in artificial intelligence (AI)
research. However, the widespread use of LLMs is also coupled with significant ethical and …

The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …