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arXiv · 2602.03652

RAGTurk: Best Practices for Retrieval Augmented Generation in Turkish

Süha Kağan Köse·Mehmet Can Baytekin·Burak Aktaş·Bilge Kaan Görür·Evren Ayberk Munis·Deniz Yılmaz·Muhammed Yusuf Kartal·Çağrı Toraman
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Abstract

Retrieval-Augmented Generation (RAG) enhances LLM factuality, yet design guidance remains English-centric, limiting insights for morphologically rich languages like Turkish. We address this by constructing a comprehensive Turkish RAG dataset derived from Turkish Wikipedia and CulturaX, comprising question-answer pairs and relevant passage chunks. We benchmark seven stages of the RAG pipeline, from query transformation and reranking to answer refinement, without task-specific fine-tuning. Our results show that complex methods like HyDE maximize accuracy (85%) that is considerably higher than the baseline (78.70%). Also a Pareto-optimal configuration using Cross-encoder Reranking and Context Augmentation achieves comparable performance (84.60%) with much lower cost. We further demonstrate that over-stacking generative modules can degrade performance by distorting morphological cues, whereas simple query clarification with robust reranking offers an effective solution.

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Süha Kağan Köse, Mehmet Can Baytekin, Burak Aktaş, Bilge Kaan Görür, Evren Ayberk Munis, Deniz Yılmaz, Muhammed Yusuf Kartal, Çağrı Toraman. 2026-02-03. RAGTurk: Best Practices for Retrieval Augmented Generation in Turkish. https://arxiv.org/abs/2602.03652

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