Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts (2025.findings-emnlp)
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| Challenge: | Large vision-language models have demonstrated strong capabilities in open-world visual understanding, but it is not clear how they address demographic biases in real life. |
| Approach: | They propose a method to assess visual fairness in LVLMs by question-answering/classification tasks. |
| Outcome: | The proposed approach improves transparency and offers a scalable solution for fairness mitigation. |
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