S Caton, C Haas - ACM Computing Surveys, 2024 - dl.acm.org
When Machine Learning technologies are used in contexts that affect citizens, companies as well as researchers need to be confident that there will not be any unexpected social …
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 …
The mixture proportions of pretraining data domains (eg, Wikipedia, books, web text) greatly affect language model (LM) performance. In this paper, we propose Domain Reweighting …
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 …
Selecting a suitable pretraining dataset is crucial for both general-domain (eg, GPT-3) and domain-specific (eg, Codex) language models (LMs). We formalize this problem as selecting …
We survey 146 papers analyzing" bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that …
Automated speech recognition (ASR) systems, which use sophisticated machine-learning algorithms to convert spoken language to text, have become increasingly widespread …
PW Koh, S Sagawa, H Marklund… - International …, 2021 - proceedings.mlr.press
Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …
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 …