[2511.09396] Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque

Computer Science > Computation and Language arXiv:2511.09396 (cs) [Submitted on 12 Nov 2025 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque Authors:Lukas Arana, Julen Etxaniz, Ander Salaberria, Gorka Azkune View a PDF of the paper titled Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque, by Lukas Arana and 3 other authors View PDF HTML (experimental) Abstract:Current Multimodal Large Language Models exhibit very strong performance for several demanding tasks. While commercial MLLMs deliver acceptable performance in low-resource languages, comparable results remain unattained within the open science community. In this paper, we aim to develop a strong MLLM for a low-resource language, namely Basque. For that purpose, we develop our own training and evaluation image-text datasets. Using two different Large Language Models as backbones, the Llama-3.1-Instruct model and a Basque-adapted variant called Latxa, we explore several data mixtures for training. We show that: i) low ratios of Basque multimodal data (around 20%) are already enough to obtain solid results on Basque benchmarks, and ii) contrary to expected, a Basque instructed backbone LLM is not required to obtain a strong MLLM in Basque. Our results pave the way to develop MLLMs for other low-resource languages by openly releasing our resources. Subjects: Computation and Language (cs.CL); Arti...