Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2022
Abstract
In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six representative pre-trained vision-language models demonstrate that stereotypical biases clearly exist in most of these models and across all four bias categories, with gender bias slightly more evident. Further analysis using gender bias data and two vision-language models also suggest that both intra-modal and inter-modal bias exist.
Discipline
Databases and Information Systems | Programming Languages and Compilers
Research Areas
Data Science and Engineering
Publication
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Virtual Conference, 2022 November 21-24
First Page
527
Last Page
538
Publisher
Association for Computational Linguistics
City or Country
Virtual Conference
Citation
ZHOU, Kankan; LAI, Yibin; and JIANG, Jing.
VLStereoSet: A study of stereotypical bias in pre-trained vision-language models. (2022). Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, Virtual Conference, 2022 November 21-24. 527-538.
Available at: https://ink.library.smu.edu.sg/sis_research/7617
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aclanthology.org/2022.aacl-main.40