This paper investigates the direct risks and harms associated with modern text-to-image
generative models, such as DALL-E and Midjourney, through a comprehensive literature …

Foundation models hold great promise for analyzing single-cell omics data, yet various
challenges remain that require further advancements. In this Comment, we discuss the …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

The mixture proportions of pretraining data domains (eg, Wikipedia, books, web text) greatly
affect language model (LM) performance. In this paper, we propose Domain Reweighting …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Large language models (LLMs) may not equitably represent diverse global perspectives on
societal issues. In this paper, we develop a quantitative framework to evaluate whose …

Large language models (LLMs) are capable of successfully performing many language
processing tasks zero-shot (without training data). If zero-shot LLMs can also reliably classify …

In response to rising concerns surrounding the safety, security, and trustworthiness of
Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red …

Large language models (LLMs) are now being used in a wide variety of contexts, including
as creativity support tools (CSTs) intended to help their users come up with new ideas. But …

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 …