Abstract Machine learning models (eg, speech recognizers) trained on average loss suffer
from representation disparity—minority groups (eg, non-native speakers) carry less weight in …

Scaling up language models has been shown to predictably improve performance and
sample efficiency on a wide range of downstream tasks. This paper instead discusses an …

Controlling the behavior of language models (LMs) without re-training is a major open
problem in natural language generation. While recent works have demonstrated successes on …

Overparameterized neural networks can be highly accurate on average on an iid test set yet
consistently fail on atypical groups of the data (eg, by learning spurious correlations that …

We estimate stochastic processes that govern the dynamics of evolving populations such as
cell differentiation. The problem is challenging since longitudinal trajectory measurements …

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 …

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 …

Large language models (LLMs) such as ChatGPT have seen widespread adoption due to
their ability to follow user instructions well. Developing these LLMs involves a complex yet …

Large language models (LLMs) have shown promise for automatic summarization but the
reasons behind their successes are poorly understood. By conducting a human evaluation on …

Abstract Language models (LMs) are increasingly being used in open-ended contexts, where
the opinions they reflect in response to subjective queries can have a profound impact, …