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Small Language Models for Less-Resourced Languages in a Real-World Scenario: The Case for Catalan

Small Language Models (SLMs), typically ranging from a few million to 10–15 billion parameters, offer a promising solution towards constraints imposed by platform size–particularly mobile and IoT devices–and by the requirements of many organizations such as SMEs, which need solutions that ensure data privacy while remaining cost-effective. Their compactness and efficiency provide digital sovereignty and flexibility, though with more limited general-purpose capabilities. This makes them especially sensitive when working with underrepresented languages, such as Catalan, due to interference from majority languages that can increase bias risk. This paper evaluates state-of-the-art SLMs in a real-world Catalan use case: an AI assistant for older adults, assessing both user interactions and structured function call generation. Our work, which contributes to the Anonymized-Project initiative for deploying a connected SLM-based infrastructure under the Model Context Protocol, demonstrates that some SLMs are able to deliver high-quality performance even in resource-constrained, linguistically minority environments.