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

Cilt: 11 Sayı: 2 31 Aralık 2025
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RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS

Öz

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.

Anahtar Kelimeler

Kaynakça

  1. Zhou, H., Hu, C., Yuan, Y., Cui, Y., Jin, Y., Chen, C., Wu, H., Yuan, D., Jiang, L., Wu, D., Liu, X., Zhang, J., Wang, X., and Liu, J., "Large language model (LLM) for telecommunications: A comprehensive survey on principles, key techniques, and opportunities", IEEE Communications Surveys & Tutorials, Vol. 27, No. 3, 1955-2005, 2025.
  2. Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., and Zhang, Y., "A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly", High-Confidence Computing, Vol. 4, No. 2, 100211, 2024.
  3. Li, X., Wang, S., Zeng, S., Wu, Y., and Yang, Y., "A survey on LLM-based multi-agent systems: Workflow, infrastructure, and challenges", Vicinagearth, Vol. 1, No. 1, 9, 2024.
  4. Yue, M., "A survey of large language model agents for question answering", arXiv preprint arXiv:2503.19213, 2025.
  5. Gao, M., Hu, X., Yin, X., Ruan, J., Pu, X., and Wan, X., "LLM-based NLG evaluation: Current status and challenges", Computational Linguistics, 1-27, 2025.
  6. Laskar, M. T. R., Alqahtani, S., Bari, M. S., Rahman, M., Khan, M. A. M., Khan, H., Jahan, I., Bhuiyan, A., Tan, C. W., Parvez, M. R., and others, "A systematic survey and critical review on evaluating large language models: Challenges, limitations, and recommendations", arXiv preprint arXiv:2407.04069, 2024.
  7. Matarazzo, A., and Torlone, R., "A survey on large language models with some insights on their capabilities and limitations", arXiv preprint arXiv:2501.04040, 2025.
  8. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Kuksa, P., Minervini, P., Yih, W.-t., Rocktäschel, T., Riedel, S., and Kiela, D., "Retrieval-augmented generation for knowledge-intensive NLP tasks", Advances in Neural Information Processing Systems (NeurIPS), 2020.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Pekiştirmeli Öğrenme

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

31 Aralık 2025

Gönderilme Tarihi

9 Eylül 2025

Kabul Tarihi

3 Kasım 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 11 Sayı: 2

Kaynak Göster

APA
Atagün, E., Güllü, M., Biroğul, S., & Barışçı, N. (2025). RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS. Mugla Journal of Science and Technology, 11(2), 56-65. https://doi.org/10.22531/muglajsci.1781095
AMA
1.Atagün E, Güllü M, Biroğul S, Barışçı N. RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS. MJST. 2025;11(2):56-65. doi:10.22531/muglajsci.1781095
Chicago
Atagün, Ercan, Merve Güllü, Serdar Biroğul, ve Necaattin Barışçı. 2025. “RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS”. Mugla Journal of Science and Technology 11 (2): 56-65. https://doi.org/10.22531/muglajsci.1781095.
EndNote
Atagün E, Güllü M, Biroğul S, Barışçı N (01 Aralık 2025) RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS. Mugla Journal of Science and Technology 11 2 56–65.
IEEE
[1]E. Atagün, M. Güllü, S. Biroğul, ve N. Barışçı, “RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS”, MJST, c. 11, sy 2, ss. 56–65, Ara. 2025, doi: 10.22531/muglajsci.1781095.
ISNAD
Atagün, Ercan - Güllü, Merve - Biroğul, Serdar - Barışçı, Necaattin. “RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS”. Mugla Journal of Science and Technology 11/2 (01 Aralık 2025): 56-65. https://doi.org/10.22531/muglajsci.1781095.
JAMA
1.Atagün E, Güllü M, Biroğul S, Barışçı N. RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS. MJST. 2025;11:56–65.
MLA
Atagün, Ercan, vd. “RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS”. Mugla Journal of Science and Technology, c. 11, sy 2, Aralık 2025, ss. 56-65, doi:10.22531/muglajsci.1781095.
Vancouver
1.Ercan Atagün, Merve Güllü, Serdar Biroğul, Necaattin Barışçı. RETRIEVAL-AUGMENTED GENERATION IN TURKISH NATURAL LANGUAGE UNDERSTANDING: A COMPARATIVE STUDY OF LARGE LANGUAGE MODELS. MJST. 01 Aralık 2025;11(2):56-65. doi:10.22531/muglajsci.1781095

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Mugla Journal of Science and Technology (MJST) dergisi Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.