# Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque Source: [Multimodal Large Language Models for Low-Resource Languages: A Case Study for Basque]() Collection index: [llms.txt]() Data updated: 2026-03-06T05:00:05.533Z Authors: Lukas Arana, Julen Etxaniz, Ander Salaberria, Gorka Azkune Published: 2025-11-12T15:08:37.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Multimodal Open-ended Evaluation on WildVision (test)](): Multimodal Open-ended Evaluation; WildVision (test); Latxa Win %: 31.44. ## Original sources and implementation [Original paper]() [PDF]()