Abstract Foundation and Large Language Models (FLLMs) are models that are trained using
a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs …

Large language models (LLMs) have demonstrated impressive capabilities in natural
language understanding and generation, but the quality bar for medical and clinical …

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 …

As the scope of machine learning broadens, we observe a recurring theme of algorithmic
monoculture: the same systems, or systems that share components (eg datasets, models) …

An introduction to the intellectual foundations and practical utility of the recent work on
fairness and machine learning. Fairness and Machine Learning introduces advanced …

Research in machine learning fairness has historically considered a single binary
demographic attribute; however, the reality is of course far more complicated. In this work …

Image captioning is an important task for benchmarking visual reasoning and for enabling
accessibility for people with vision impairments. However, as in many machine learning …

Recent scholarship has brought attention to the fact that there often exist multiple models for
a given prediction task with equal accuracy that differ in their individual-level predictions or …

Large language models (LLMs) have led to a surge in collaborative writing with model
assistance. As different users incorporate suggestions from the same model, there is a risk of …

Abstract Machine learning is traditionally studied at the model level: researchers measure
and improve the accuracy, robustness, bias, efficiency, and other dimensions of specific …