A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

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 …

Most traditional AI safety research has approached AI models as machines and centered on
algorithm-focused attacks developed by security experts. As large language models (LLMs) …

Language models (LMs) have become ubiquitous in both NLP research and in commercial
product offerings. As their commercial importance has surged, the most powerful models …

Task automation has been greatly empowered by the recent advances in Large Language
Models (LLMs) via Python code, where the tasks ranging from software engineering …

Web-crawled pretraining datasets underlie the impressive" zero-shot" evaluation
performance of multimodal models, such as CLIP for classification and Stable-Diffusion for …

Alongside an explosion in research and development related to large language models,
there has been a concomitant rise in the creation of pretraining datasets—massive …

AI progress is creating a growing range of risks and opportunities, but it is often unclear how
they should be navigated. In many cases, the barriers and uncertainties faced are at least …

Bias amplification is a phenomenon in which models increase imbalances present in the
training data. In this paper, we study bias amplification in the text-to-image domain using …

Language models (LMs) derive their capabilities from extensive training on diverse data,
including potentially copyrighted material. These models can memorize and generate …