@inproceedings{sauri-etal-2026-small,
    title = "Small Language Models for Less-Resourced Languages in a Real-World Scenario: The Case for {C}atalan",
    author = "Saur{\'i}, Roser  and
      S{\`a}nchez-Ferreres, Josep  and
      Padro, Lluis  and
      Carmona, Josep",
    editor = "Ojha, Atul Kr.  and
      Sakti, Sakriani  and
      Soria, Claudia  and
      Melero, Maite  and
      McCrae, John P.  and
      Lignos, Constantine  and
      Liu, Chao-Hong  and
      Claramunt, German Rigau  and
      Rehm, Georg",
    booktitle = "Proceedings of the {SIGUL} 2026 Joint Workshop with {ELE}, {EURALI}, and {DCLRL}: Towards Inclusivity and Equality: Language Resources and Technologies for Under-Resourced and Endangered Languages",
    month = may,
    year = "2026",
    address = "Palma, Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.sigul-1.27/",
    doi = "10.63317/4qn4qiyiwuze",
    pages = "270--279",
    abstract = "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."
}