WorldBench: Quantifying Geographic Disparities in LLM Factual Recall

M Moayeri, E Tabassi, S Feizi - The 2024 ACM Conference on Fairness …, 2024 - dl.acm.org
As large language models (LLMs) continue to improve and gain popularity, some may use
the models to recall facts, despite well documented limitations with LLM factuality. Towards …

Stable diffusion exposed: Gender bias from prompt to image

Y Wu, Y Nakashima, N Garcia - arXiv preprint arXiv:2312.03027, 2023 - arxiv.org
Recent studies have highlighted biases in generative models, shedding light on their
predisposition towards gender-based stereotypes and imbalances. This paper contributes to …

Towards Geographic Inclusion in the Evaluation of Text-to-Image Models

M Hall, SJ Bell, C Ross, A Williams… - The 2024 ACM …, 2024 - dl.acm.org
Rapid progress in text-to-image generative models coupled with their deployment for visual
content creation has magnified the importance of thoroughly evaluating their performance …

Consistency-diversity-realism Pareto fronts of conditional image generative models

P Astolfi, M Careil, M Hall, O Mañas, M Muckley… - arXiv preprint arXiv …, 2024 - arxiv.org
Building world models that accurately and comprehensively represent the real world is the
utmost aspiration for conditional image generative models as it would enable their use as …