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dc.contributor.authorEscarda Fernández, Miguel
dc.contributor.authorEiras-Franco, Carlos
dc.contributor.authorCancela, Brais
dc.contributor.authorGuijarro-Berdiñas, Bertha
dc.contributor.authorAlonso-Betanzos, Amparo
dc.date.accessioned2024-11-14T13:30:28Z
dc.date.available2024-11-14T13:30:28Z
dc.date.issued2025-03
dc.identifier.citationM. Escarda, C. Eiras-Franco, B. Cancela, B. Guijarro-Berdiñas, and A. Alonso-Betanzos, "Performance and sustainability of BERT derivatives in dyadic data", Expert Systems with Applications, Vol. 2621, article number 125647, March 2025, https://doi.org/10.1016/j.eswa.2024.125647es_ES
dc.identifier.urihttp://hdl.handle.net/2183/40124
dc.description.abstract[Abstract]: In recent years, the Natural Language Processing (NLP) field has experienced a revolution, where numerous models – based on the Transformer architecture – have emerged to process the ever-growing volume of online text-generated data. This architecture has been the basis for the rise of Large Language Models (LLMs). Enabling their application to many diverse tasks in which they excel with just a fine-tuning process that comes right after a vast pre-training phase. However, their sustainability can often be overlooked, especially regarding computational and environmental costs. Our research aims to compare various BERT derivatives in the context of a dyadic data task while also drawing attention to the growing need for sustainable AI solutions. To this end, we utilize a selection of transformer models in an explainable recommendation setting, modeled as a multi-label classification task originating from a social network context, where users, restaurants, and reviews interact.es_ES
dc.description.sponsorshipResearch funded by the Spain Ministry of Science, Innovation and Universities (MCIN/AEI/10.13039/501100011033) and ERDF “A way of making Europe” (PID2019-109238GB-C22, PID2021-128045OA-I00, PID2023-147404OB-I00), by the “Xunta de Galicia” (ED431C 2022/44) with the European Union ERDF funds, and by the Ministry for Digital Transformation and Civil Service and “Next-GenerationEU”/PRTR (Grant TSI-100925-2023-1). CITIC, as Research Center accredited by Galician University System, is funded by “Consellería de Cultura, Educación e Universidade from Xunta de Galicia ”, supported in an 80% through ERDF Operational Programme Galicia 2014-2020, and the remaining 20% by “Secretaría Xeral de Universidades” (Grant ED431G 2023/01).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.language.isoenges_ES
dc.publisherElsevier Ltdes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLEes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128045OA-I00/ES/APRENDIZAJE PROFUNDO ETICOes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-147404OB-I00/ES/APRENDIZAJE AUTOMATICO FRUGAL: POTENCIANDO LA IA EN ENTORNOS CON RECURSOS LIMITADOS PARA LOS DESAFIOS DEL MUNDO REALes_ES
dc.relationinfo:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDESes_ES
dc.relation.urihttps://doi.org/10.1016/j.eswa.2024.125647es_ES
dc.rightsAttribution 4.0 International (CC BY)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectBERTes_ES
dc.subjectDyadic dataes_ES
dc.subjectSustainable AIes_ES
dc.subjectTransformeres_ES
dc.titlePerformance and sustainability of BERT derivatives in dyadic dataes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleExpert Systems with Applicationses_ES
UDC.volume262es_ES
UDC.issue125647es_ES
dc.identifier.doi10.1016/j.eswa.2024.125647
UDC.coleccionInvestigación
UDC.departamentoCiencias da Computación e Tecnoloxías da Información
UDC.grupoInvLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA)


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