Optimal RAG System Design for Turkish Textbooks: A Comprehensive Evaluation and Performance Enhancement Study
2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025, Bursa, Türkiye, 10 - 12 Eylül 2025, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/asyu67174.2025.11208464
- Basıldığı Şehir: Bursa
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: educational AI systems, RAG systems, retrieval-augmented generation, textbook question-answering, Turkish natural language processing
- Yıldız Teknik Üniversitesi Adresli: Evet
Özet
Retrieval-augmented generation enables precise educational question-answering by combining retrieval with natural language generation. Limited evaluation exists for educational RAG systems, particularly for curriculum-based applications in diverse languages. We introduce systematic evaluation using Turkish Ministry of Education textbooks, comparing embedding architectures, generation models, and optimization techniques across educational questions. Results establish multilingual-e5-large-instruct and BAAI/bge-m3 as optimal embedding models, demonstrate Qwen3 and Turkish-Gemma-9b's superior generation performance, and show ensemble methods yield 3.3% improvements. This provides evidence-based recommendations for educational RAG development across different languages and curricula.