RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS

Authors : Ercan Atagün, Merve Güllü, Serdar Biroğul, Necaattin Barışçı

Pages : 56-65

Doi:10.22531/muglajsci.1781095

View : 151 | Download : 414

Publication Date : 2025-12-31

Article Type : Research Paper

Abstract :Large Language Models (LLMs) have markedly progressed natural language processing. Nevertheless, owing to the restricted availability of training data, they may prove insufficient in generating current and precise information, particularly for low-resource languages. The Retrieval-Augmented Generation (RAG) methodology, designed to resolve this challenge, improves the precision and dependability of models\\\' outputs by leveraging external information sources. This study comparatively evaluated four distinct LLMs (Qwen-14B, Gemma3-12B, LLaMA3.1-8B, and DeepSeek-R1-14B) within the RAG framework using a Turkish question-answer dataset. Experimental results demonstrate the RAG methodology markedly enhances information precision, response uniformity, and contextual relevance in Turkish question-answering systems. Moreover, the LLaMA3.1-8B model had the best equitable performance regarding precision and recall. The findings illustrate the relevance of RAG-based applications for Turkish and offer significant insights for advancing knowledge-assisted generation methods. This study addresses a significant gap in the literature by illustrating the viability of RAG-based systems in morphologically rich and low-resource languages, including Turkish. It serves as a foundational reference for subsequent Turkish natural language processing research.
Keywords : Büyük dil modelleri, Geri alma-artırılmış üretim, Türkçe GAÜ

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