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dc.contributor.authorRusinov, Volodymyr-
dc.contributor.authorBasenko, Nikita-
dc.date.accessioned2025-06-18T09:51:57Z-
dc.date.available2025-06-18T09:51:57Z-
dc.date.issued2025-04-26-
dc.identifier.urihttps://opendata.uni-halle.de//handle/1981185920/121183-
dc.identifier.urihttp://dx.doi.org/10.25673/119225-
dc.description.abstractThis study explores the potential of Small Language Models (SLMs) as an efficient and secure alternative to larger models like GPT-4 for various natural language processing (NLP) tasks. With growing concerns around data privacy and the resource-intensiveness of large models, SLMs present a promising solution for research and applications requiring fast, cost-effective, and locally deployable models. The research evaluates several SLMs across tasks such as translation, summarization, Named Entity Recognition (NER), text generation, classification, and retrieval-augmented generation (RAG), comparing their performance against larger counterparts. Models were assessed using a range of metrics specific to the intended task. Results show that smaller models perform well on complex tasks, often rivalling or even outperforming larger models like Phi-3.5. The study concludes that SLMs offer an optimal trade-off between performance and computational efficiency, particularly in environments where data security and resource constraints are critical. The findings highlight the growing viability of smaller models for a wide range of real-world applications.-
dc.format.extent1 Online-Ressource (6 Seiten)-
dc.language.isoeng-
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0/-
dc.subject.ddcDDC::6** Technik, Medizin, angewandte Wissenschaften::60* Technik::600 Technik, Technologie-
dc.titleExploration of the Efficiency of SLM-Enabled Platforms for Everyday Tasks-
local.versionTypepublishedVersion-
local.publisher.universityOrInstitutionHochschule Anhalt-
local.openaccesstrue-
dc.identifier.ppn1927936381-
cbs.publication.displayform2025-
local.bibliographicCitation.year2025-
cbs.sru.importDate2025-06-18T09:51:02Z-
local.bibliographicCitationEnthalten in Proceedings of the 13th International Conference on Applied Innovations in IT - Koethen, Germany : Edition Hochschule Anhalt, 2025-
local.accessrights.dnbfree-
Enthalten in den Sammlungen:International Conference on Applied Innovations in IT (ICAIIT)

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