Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models

• URL: http://arxiv.org/abs/2104.08666v1
• Date: Sun, 18 Apr 2021 00:02:32 GMT
• Title: Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models
• Authors: Tejas Srinivasan, Yonatan Bisk
• Abstract summary: This work extends text-based bias analysis methods to investigate multimodal language models. We demonstrate that VL-BERT exhibits gender biases, often preferring to reinforce a stereotype over faithfully describing the visual scene.
• Score: 17.90351661475405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Numerous works have analyzed biases in vision and pre-trained language models individually - however, less attention has been paid to how these biases interact in multimodal settings. This work extends text-based bias analysis methods to investigate multimodal language models, and analyzes intra- and inter-modality associations and biases learned by these models. Specifically, we demonstrate that VL-BERT (Su et al., 2020) exhibits gender biases, often preferring to reinforce a stereotype over faithfully describing the visual scene. We demonstrate these findings on a controlled case-study and extend them for a larger set of stereotypically gendered entities.

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