Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
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Computer Science > Computation and Language
Title:Multilingual and Multimodal LLMs in the Wild: Building for Low-Resource Languages
Abstract:Multimodal LLMs are evolving from vision-language to tri-modality that see, hear, and read, yet pipelines and benchmarks remain English-centric and compute-heavy. The tutorial offers an overview of this emerging research area for multilingual multimodality across text, speech, and vision under limited data/compute budgets, synthesizing foundations, recent multilingual models (PALO, Maya), speech-text LLMs. We cover low-cost data creation/curation; adapter stacks for tri-modal alignment; culture-aware evaluation beyond English and hands on resources for fine-tuning a compact multilingual VLM and wiring a speech->text->LLM pipeline. The content will be delivered as an interactive half-day tutorial, designed for researchers and practitioners working on multilingual, multimodal AI in low-resource language settings.
| Comments: | Multimodal Foundation Models, Large Language Models, Native, Multilingual, Language Diversity, Low-resources-language |
| Subjects: | Computation and Language (cs.CL) |
| MSC classes: | 68T50 |
| ACM classes: | F.2.2; I.2.7 |
| Cite as: | arXiv:2605.17152 [cs.CL] |
| (or arXiv:2605.17152v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.17152
arXiv-issued DOI via DataCite (pending registration)
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