The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning

• URL: http://arxiv.org/abs/2411.11758v1
• Date: Mon, 18 Nov 2024 17:37:10 GMT
• Title: The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning
• Authors: Longju Bai, Angana Borah, Oana Ignat, Rada Mihalcea,
• Abstract summary: We introduce MosAIC, a framework to enhance cross-cultural image captioning using LMMs with distinct cultural personas. We provide a dataset of culturally enriched image captions in English for images from China, India, and Romania. We show that the multi-agent interaction outperforms single-agent models across different metrics.
• Score: 25.956176241542597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Multimodal Models (LMMs) exhibit impressive performance across various multimodal tasks. However, their effectiveness in cross-cultural contexts remains limited due to the predominantly Western-centric nature of most data and models. Conversely, multi-agent models have shown significant capability in solving complex tasks. Our study evaluates the collective performance of LMMs in a multi-agent interaction setting for the novel task of cultural image captioning. Our contributions are as follows: (1) We introduce MosAIC, a Multi-Agent framework to enhance cross-cultural Image Captioning using LMMs with distinct cultural personas; (2) We provide a dataset of culturally enriched image captions in English for images from China, India, and Romania across three datasets: GeoDE, GD-VCR, CVQA; (3) We propose a culture-adaptable metric for evaluating cultural information within image captions; and (4) We show that the multi-agent interaction outperforms single-agent models across different metrics, and offer valuable insights for future research. Our dataset and models can be accessed at https://github.com/MichiganNLP/MosAIC.

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