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 …

Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

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 …

We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model
with 130 billion parameters. It is an attempt to open-source a 100B-scale model at least as …

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 …

As humans, we navigate a multimodal world, building a holistic understanding from all our
senses. We introduce MERLOT Reserve, a model that represents videos jointly over time …

Generative AI systems across modalities, ranging from text, image, audio, and video, have
broad social impacts, but there exists no official standard for means of evaluating those …

Understanding the landscape of potential harms from algorithmic systems enables
practitioners to better anticipate consequences of the systems they build. It also supports the …

Designing robust text-to-image (T2I) models have been extensively explored in recent years,
especially with the emergence of diffusion models, which achieves state-of-the-art results on …

Design biases in NLP systems, such as performance differences for different populations,
often stem from their creator's positionality, ie, views and lived experiences shaped by …