Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Vision-Language Models
<|reference_start|>Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective Shifts in Vision-Language Models: Unequal representation across cultures and socioeconomic groups in AI is a significant and challenging problem, often leading to uneven model performance. As a step toward addressing this issue, we formulate translated non-English, geographic, and socioeconomic integrated prompts and evaluate their impact on VL model performance for data from different countries and income groups. Our findings show that geographic and socioeconomic integrated prompts improve VL performance on lower-income data and favor the retrieval of topic appearances commonly found in data from low-income households. From our analyses, we identify and highlight contexts where these strategies yield the most improvements. Our model analysis code is publicly available at https://github.com/Anniejoan/Uplifting-Lower-income-data .<|reference_end|>
@article{nwatu2024uplifting,
    title={Uplifting Lower-Income Data: Strategies for Socioeconomic Perspective
  Shifts in Large Multi-modal Models},
    author={Joan Nwatu, Oana Ignat, Rada Mihalcea},
    journal={arXiv preprint arXiv:2407.02623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02623},
    primaryClass={cs.CY cs.AI cs.CL cs.CV}
}