The development of medical applications of machine learning has required manual
annotation of data, often by medical experts. Yet, the availability of large-scale unannotated …

As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

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 …

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 …

Participatory approaches to artificial intelligence (AI) and machine learning (ML) are gaining
momentum: the increased attention comes partly with the view that participation opens the …

We identify label errors in the test sets of 10 of the most commonly-used computer vision,
natural language, and audio datasets, and subsequently study the potential for these label …

We introduce MADLAD-400, a manually audited, general domain 3T token monolingual
dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations …

AbstractScale the model, scale the data, scale the compute'is the reigning sentiment in the
world of generative AI today. While the impact of model scaling has been extensively …

Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …

Data is a crucial component of machine learning. The field is reliant on data to train, validate,
and test models. With increased technical capabilities, machine learning research has …