Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Tiwari, Satyam; Kujur, Lawrence; Shanbhog, Manjula Title A name given to the resource Enhancing Regional Language Proficiency in Large Language Models Through Translated Datasets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1741 LNNS;pp.332-347 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-12827-0_26" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-12827-0_26</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027588525?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027588525?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Tiwari S., Christ (Deemed to Be University), School of Science, NCR, Delhi, India; Kujur L., Christ (Deemed to Be University), School of Science, NCR, Delhi, India; Shanbhog M., Christ (Deemed to Be University), School of Science, NCR, Delhi, India Description An account of the resource Although Large Language Models (LLMs) have made significant progress in Natural Language Processing the lack of high-quality training data frequently limits their ability to perform well in regional languages. To improve LLM competency this study methodically translates an English dataset into the low-resource language of Bhojpuri. On this new dataset we apply a structured translation methodology and then refine an LLM that has already been trained. The models capacity to produce contextually relevant and culturally appropriate responses in Bhojpuri has significantly improved according to a comparison of its performance before and after fine-tuning. Our findings show that this translation-centric approach provides a practical and affordable way to enhance the usefulness and inclusivity of LLMs increasing the effectiveness and accessibility of these potent AI tools for underrepresented linguistic groups globally. For linguistic groups that are marginalized globally. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Dataset Translation; Fine-tuning; Large Language Models; Machine Translation; Natural Language Processing; Regional Languages Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-303212826-3; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online