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arXiv · 2508.05502

MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs

Yufei Gao·Jiaying Fei·Nuo Chen·Ruirui Chen·Guohang Yan·Yunshi Lan·Botian Shi
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Abstract

Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings. We argue that this failure is not merely a linguistic limitation: culture-specific visual knowledge depends on native visual-textual alignments that translation-centric pipelines rarely provide. We present MELLA, a multimodal dataset across eight low-resource languages, designed to support linguistic fluency and cultural groundedness. MELLA uses a dual-source strategy that combines native web image-alt-text pairs for culture-grounded supervision with generated-and-translated image descriptions for linguistically rich supervision, explicitly separating two learning signals often conflated in multilingual multimodal data. Through controlled diagnostic fine-tuning on multiple MLLM backbones, we show that MELLA mitigates cultural hallucination by helping models recognize and articulate culturally specific entities overlooked by translation-based adaptation. Our findings highlight data alignment, rather than model modification alone, as a path toward culturally grounded multimodal understanding in low-resource languages. The dataset is available at https://opendatalab.com/applyMultilingualCorpus.

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Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi. 2025-08-07. MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs. https://arxiv.org/abs/2508.05502

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