Alpdemir, YusufAlpdemir, Mahmut Nedim2025-02-222025-02-222024-06-23979-8-3503-8489-5https://hdl.handle.net/11693/116614Conference Name:2024 IEEE international conference on advanced systems and emergent technologies, icaset 2024In 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.EnglishMachine learningLanguage modelsText generationText classificationCustomized content generationTransformer architecturesConference Paper10.1109/IC_ASET61847.2024.10596255