A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning
Abstract
Vision-language models can encode societal biases and stereotypes, but there are challenges to measuring and mitigating these multimodal harms due to lacking measurement robustness and feature degradation. To address these challenges, we investigate bias measures and apply ranking metrics for image-text representations. We then investigate debiasing methods and show that prepending learned embeddings to text queries that are jointly trained with adversarial debiasing and a contrastive loss reduces various bias measures with minimal degradation to the image-text representation.
Cite
@article{arxiv.2203.11933,
title = {A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning},
author = {Hugo Berg and Siobhan Mackenzie Hall and Yash Bhalgat and Wonsuk Yang and Hannah Rose Kirk and Aleksandar Shtedritski and Max Bain},
journal= {arXiv preprint arXiv:2203.11933},
year = {2022}
}
Comments
17 pages, 4 figures, 7 tables. For code and trained token embeddings, see https://github.com/oxai/debias-vision-lang; Changed to use ACL layout, added joint training with comparison figure, corrected spelling and formatting errors; This paper is accepted for publication at AACL 2022, the official version of record is in the ACL Anthology