SubmissionNumber#=%=#21
FinalPaperTitle#=%=#Can LLMs Handle Low-Resource Dialects? A Case Study on Translation and Common Sense Reasoning in Šariš
ShortPaperTitle#=%=#
NumberOfPages#=%=#10
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#While Large Language Models (LLMs) have demonstrated considerable potential in advancing natural language processing in dialect-specific contexts, their effectiveness in these settings has yet to be thoroughly assessed. This study introduces a case study on Šariš, a dialect of Slovak, which is itself a language with fewer resources, focusing on Machine Translation and Common Sense Reasoning tasks. We employ LLMs in a zero-shot configuration and for data augmentation to refine Slovak-Šariš and Šariš-Slovak translation models. The accuracy of these models is then manually verified by native speakers. Additionally, we introduce ŠarišCOPA, a new dataset for causal common sense reasoning, which, alongside SlovakCOPA, serves to evaluate LLM's performance in a zero-shot framework. Our findings highlight LLM's capabilities in processing low-resource dialects and suggest a viable approach for initiating dialect-specific translation models in such contexts.
Author{1}{Firstname}#=%=#Viktória
Author{1}{Lastname}#=%=#Ondrejová
Author{1}{Email}#=%=#ondrejovaviktoria@gmail.com
Author{1}{Affiliation}#=%=#Comenius University in Bratislava
Author{2}{Firstname}#=%=#Marek
Author{2}{Lastname}#=%=#Suppa
Author{2}{Username}#=%=#mareksuppa
Author{2}{Email}#=%=#softconf@mareksuppa.com
Author{2}{Affiliation}#=%=#Comenius University in Bratislava

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