Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918037066612736 |
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| author | Seller, Luís Couto Torres, Íñigo Sanz Vogel-Fernández, Adrián Carballo, Carlos González Sánchez, Pedro Miguel Sánchez Martín, Adrián Carruana Ambite, Enrique de Miguel |
| author_facet | Seller, Luís Couto Torres, Íñigo Sanz Vogel-Fernández, Adrián Carballo, Carlos González Sánchez, Pedro Miguel Sánchez Martín, Adrián Carruana Ambite, Enrique de Miguel |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_03312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices Seller, Luís Couto Torres, Íñigo Sanz Vogel-Fernández, Adrián Carballo, Carlos González Sánchez, Pedro Miguel Sánchez Martín, Adrián Carruana Ambite, Enrique de Miguel Computation and Language Machine Learning 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 |
| title | Evaluating Compact LLMs for Zero-Shot Iberian Language Tasks on End-User Devices |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2504.03312 |