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 …

Generative audio models typically focus their applications in music and speech generation,
with recent models having human-like quality in their audio output. This paper conducts a …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

Images generated by diffusion models like Stable Diffusion are increasingly widespread.
Recent works and even lawsuits have shown that these models are prone to replicating their …

Modern neural language models that are widely used in various NLP tasks risk memorizing
sensitive information from their training data. Understanding this memorization is important …

The legality of training language models (LMs) on copyrighted or otherwise restricted data is
under intense debate. However, as we show, model performance significantly degrades if …

The race to train language models on vast, diverse, and inconsistently documented datasets
has raised pressing concerns about the legal and ethical risks for practitioners. To remedy …

Generative AI has witnessed rapid advancement in recent years, expanding their
capabilities to create synthesized content such as text, images, audio, and code. The high …

Large language models (LLMs) have made transformed changes for human society. One of
the key computation in LLMs is the softmax unit. This operation is important in LLMs …

Diffusion models are a class of generative models that serve to establish a stochastic
transport map between an empirically observed, yet unknown, target distribution and a …