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Oguz Dikenelli

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Conference Jul 2026

Instruction-Tuned Translation Language Model Focused On Turkish

Large language models have high computation and inference costs. Recently, Small Language Models (SLMs) have become more important because they require fewer resources and offer high efficiency. Different training approaches can be used for SLMs to achieve high performance, even on resource-constrained hardware.In this study, we present our MT-270M translation model. It was trained using instruction fine-tuning to provide high efficiency and success for bidirectional translation between Turkish and English. We observe how we selected the datasets for the training phase and how data selection affects translation quality. Then, we explain how we prepared our high-quality training data. Finally, we examine the effects of data quality changes and including different tasks in the training process on the success of the small language model.

Ali Efe Çoban, Oguz Dikenelli · 0 citations
Conference Jul 2026

Domain-Adapted Language Models for English-Turkish Medical Text Translation: An Evaluation of Zero-Shot, Few-Shot, and Fine-Tuning Strategies

This study investigates the English-to-Turkish clinical translation performance of three large language models (Aya-23, Seed-X-Instruct, Gemma-3) using zero/few-shot prompting and QLoRA fine-tuning. Our three-stage setup evaluates: (1) general translation after FLORES-devtest training, (2) medical domain adaptation on i2b2 via a mixed dataset, and (3) generalization capacity on the independent FLORES-test set after mixed-data fine-tuning, using SacreBLEU, chrF, and COMET. Results show that Seed-X-Instruct achieves the strongest fine-tuned performance, reaching 32.70 BLEU, 64.34 chrF, and 90.65 COMET on the general FLORES test set, and 22.40 BLEU, 54.61 chrF, and 79.70 COMET on the medical i2b2 set. After mixed-data training, Seed-X improves from 11.48 to 32.39 BLEU on the unseen FLORES test set, whereas Aya-23 drops from 19.23 to 10.83 and Gemma-3 largely preserves its strong zero-shot baseline. These findings indicate that QLoRA effects are model-dependent and that domain adaptation does not yield the same generalization behavior across models.

Muhammetalp Erdem, Ş. Bora, Oguz Dikenelli · 0 citations