dc.contributor.author | Escarda Fernández, Miguel | |
dc.contributor.author | Eiras-Franco, Carlos | |
dc.contributor.author | Cancela, Brais | |
dc.contributor.author | Guijarro-Berdiñas, Bertha | |
dc.contributor.author | Alonso-Betanzos, Amparo | |
dc.date.accessioned | 2024-11-14T13:30:28Z | |
dc.date.available | 2024-11-14T13:30:28Z | |
dc.date.issued | 2025-03 | |
dc.identifier.citation | M. 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.2024.125647 | es_ES |
dc.identifier.uri | http://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.sponsorship | Research 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.sponsorship | Xunta de Galicia; ED431C 2022/44 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2023/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier Ltd | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLE | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128045OA-I00/ES/APRENDIZAJE PROFUNDO ETICO | es_ES |
dc.relation | info: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 REAL | es_ES |
dc.relation | info:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDES | es_ES |
dc.relation.uri | https://doi.org/10.1016/j.eswa.2024.125647 | es_ES |
dc.rights | Attribution 4.0 International (CC BY) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | BERT | es_ES |
dc.subject | Dyadic data | es_ES |
dc.subject | Sustainable AI | es_ES |
dc.subject | Transformer | es_ES |
dc.title | Performance and sustainability of BERT derivatives in dyadic data | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Expert Systems with Applications | es_ES |
UDC.volume | 262 | es_ES |
UDC.issue | 125647 | es_ES |
dc.identifier.doi | 10.1016/j.eswa.2024.125647 | |
UDC.coleccion | Investigación | |
UDC.departamento | Ciencias da Computación e Tecnoloxías da Información | |
UDC.grupoInv | Laboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA) | |