Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices
It addresses accessibility for under-resourced languages on consumer devices, but is incremental as it focuses on evaluation rather than new methods.
This work evaluated compact large language models for zero-shot tasks in Iberian languages, finding that while some models performed well, significant gaps persisted, especially for Basque.
Large Language Models have significantly advanced natural language processing, achieving remarkable performance in tasks such as language generation, translation, and reasoning. However, their substantial computational requirements restrict deployment to high-end systems, limiting accessibility on consumer-grade devices. This challenge is especially pronounced for under-resourced languages like those spoken in the Iberian Peninsula, where relatively limited linguistic resources and benchmarks hinder effective evaluation. This work presents a comprehensive evaluation of compact state-of-the-art LLMs across several essential NLP tasks tailored for Iberian languages. The results reveal that while some models consistently excel in certain tasks, significant performance gaps remain, particularly for languages such as Basque. These findings highlight the need for further research on balancing model compactness with robust multilingual performance