Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929756715352064 |
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| author | Girrbach, Leander Alaniz, Stephan Huang, Yiran Darrell, Trevor Akata, Zeynep |
| author_facet | Girrbach, Leander Alaniz, Stephan Huang, Yiran Darrell, Trevor Akata, Zeynep |
| contents | Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. Similarly, they tend to attribute more skills and positive personality traits to women than to men, and we see a consistent tendency to associate negative personality traits with men. To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance on downstream tasks. We argue for pre-deploying gender bias assessment in VLAs and motivate further development of debiasing strategies to ensure equitable societal outcomes. Code is available at https://github.com/ExplainableML/vla-gender-bias. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_19314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) Girrbach, Leander Alaniz, Stephan Huang, Yiran Darrell, Trevor Akata, Zeynep Computers and Society Computation and Language Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skills, and occupations. Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. Similarly, they tend to attribute more skills and positive personality traits to women than to men, and we see a consistent tendency to associate negative personality traits with men. To eliminate the gender bias in these models, we find that fine-tuning-based debiasing methods achieve the best trade-off between debiasing and retaining performance on downstream tasks. We argue for pre-deploying gender bias assessment in VLAs and motivate further development of debiasing strategies to ensure equitable societal outcomes. Code is available at https://github.com/ExplainableML/vla-gender-bias. |
| title | Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) |
| topic | Computers and Society Computation and Language |
| url | https://arxiv.org/abs/2410.19314 |