Comparing Chunking and Embedding Strategies for Turkish RAG Systems

arXiv:2608.26192 · cs.CL, cs.AI · Submitted 2026-08-24 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-08-24

Updated: 2026-08-28

Comments: Accepted to INTCEC 2026. This is the author's pre-print version. The final authenticated version will be available through the conference proceedings

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 26× in this paper · explained in 1 episode
• RAG — 11× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation — 6× in this paper
• Embeddings — 5× in this paper · explained in 2 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Retrieval-Augmented Generation for Large Language Models: A Survey
• Multilingual E5 Text Embeddings: A Technical Report
• Docling Technical Report
• Mecellem Models: Turkish Models Trained from Scratch and Continually Pre-trained for the Legal Domain

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