Optimizing Multilingual Embedding Models for Retrieval and Reranking in RAG Pipelines: Enhancing Semantic Search in Turkish Medical Datasets

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This study examines the effectiveness of enhanced multilingual embedding models in improving retrieval performance for Turkish medical text data. We consider two specific medical applications in Turkish language: the TUS examination, a standardized medical assessment featuring exam questions, and Clinical QA, which involves authentic patient-physician interactions. By implementing multi-stage fine-tuning protocols on domain-specialized models, we provide detailed performance assessment and explore cross-domain transfer capabilities of the trained models. Our findings indicate that domain-specialized models improve in-domain retrieval relative to generic models, and that systematic optimization through our multi-stage pipeline yields measurable gains in retrieval precision. For instance, domain-specific fine-tuning improves TUS retrieval performance from 0.69 to 0.79 in P@1 and from 0.77 to 0.85 in MRR, while Clinical QA fine-tuning with hard-negative sampling improves P@1 from 0.33 to 0.39 and MRR from 0.41 to 0.48 relative to the vanilla encoder. In addition, reranking improves P@1 from 0.788 to 0.823 in our evaluated setting, corresponding to a 4.5% relative improvement. Furthermore, we find that, in multilingual model training, domain-specific knowledge acquired in the healthcare context of one language effectively transfers and enhances performance across other languages.