Ai-assisted text composition for automated content authoring using transformer-based language models

buir.contributor.authorAlpdemir, Yusuf
dc.contributor.authorAlpdemir, Yusuf
dc.contributor.authorAlpdemir, Mahmut Nedim
dc.coverage.spatialHammamet, TUNISIA
dc.date.accessioned2025-02-22T07:59:12Z
dc.date.available2025-02-22T07:59:12Z
dc.date.issued2024-06-23
dc.departmentDepartment of Computer Engineering
dc.descriptionConference Name:2024 IEEE international conference on advanced systems and emergent technologies, icaset 2024
dc.description.abstractIn this paper, we introduce a hybrid method that combines the use of Controllable Text Generation (CTG) approach via Large Language Models (LLMs), fine-tuned language models and sentence transformers in a single framework to generate real-author styled articles in Turkish language. As such, we seek to exemplify hybrid solutions that produce real-human styled high-quality contents, given limited resources and relatively short text prompts as inputs. To achieve this, we introduce a novel method to assemble an author-specific article in different coherence and fluency levels, based on phrasal control of the CTG process. Control phrases are automatically assembled based on a semantic correlation measure calculated using sentence embed dings corresponding to author articles, that are obtained from pre-trained sentence transformers.
dc.identifier.doi10.1109/IC_ASET61847.2024.10596255
dc.identifier.isbn979-8-3503-8489-5
dc.identifier.urihttps://hdl.handle.net/11693/116614
dc.language.isoEnglish
dc.publisherIEEE
dc.relation.isversionofhttps://dx.doi.org/10.1109/IC_ASET61847.2024.10596255
dc.subjectMachine learning
dc.subjectLanguage models
dc.subjectText generation
dc.subjectText classification
dc.subjectCustomized content generation
dc.subjectTransformer architectures
dc.titleAi-assisted text composition for automated content authoring using transformer-based language models
dc.typeConference Paper

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