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 …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

Recent breakthroughs in large language models (LLMs) have centered around a handful of
data-rich languages. What does it take to broaden access to breakthroughs beyond first …

On-device machine learning (ML) promises to improve the privacy, responsiveness, and
proliferation of new, intelligent user experiences by moving ML computation onto everyday …

Quantization techniques are widely used to improve inference speed and deployment of
large language models. While a wide body of work examines the impact of quantization on …

Neural network pruning offers an effective method for compressing a multilingual automatic
speech recognition (ASR) model with minimal performance loss. However, it entails several …

We study the impact of a standard practice in compressing foundation vision-language
models-quantization-on the models' ability to produce socially-fair outputs. In contrast to …

In this thesis, the constituent works ask “What is gained or lost as we vary the number of
parameters?”. This question is increasingly relevant in an era of scientific inquiry where …