EXPLORING THE EFFECTIVENESS OF PRE-TRAINED TRANSFORMER MODELS FOR TURKISH QUESTION ANSWERING
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Recent advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI) have been propelled by the emergence of Transformer-based Large Language Models (LLMs), which have demonstrated outstanding performance across various tasks, including Question Answering (QA). However, the adoption and performance of these models in low-resource and morphologically rich languages like Turkish remain underexplored. This study addresses this gap by systematically evaluating several state-of-the-art Transformer-based LLMs on a curated, gold-standard Turkish QA dataset. The models evaluated include BERTurk, XLM-RoBERTa, ELECTRA-Turkish, DistilBERT, and T5-Small, with a focus on their ability to handle the unique linguistic challenges posed by Turkish. The experimental results indicate that the BERTurk model outperforms other models, achieving an F1-score of 0.8144, an Exact Match of 0.6351, and a BLEU score of 0.4035. The study highlights the importance of language-specific pre-training and the need for further research to improve the performance of LLMs in low-resource languages. The findings provide valuable insights for future efforts in enhancing Turkish NLP resources and advancing QA systems in underrepresented linguistic contexts.
Açıklama
Anahtar Kelimeler
Artificial intelligence, natural language processing, question answering, transformer, large language model
Kaynak
KSÜ Mühendislik Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
28
Sayı
2
