Year: 2026
Type: article
Abstract: The aim of the study was to improve the accuracy of semantic search of medical information in Russian by finetuning the RuBERT language model on the specialized RuMedDaNet dataset using the Matryoshka Representation Learning method to create compact and efficient vector representations of text. Materials and Methods. The study utilized the RuMedDaNet dataset, which contains Russian-language medical texts. Various embedding training techniques were applied to optimize performance, including the “matryoshka” approach, which enables reducing the dimensionality of vector representations without loss of quality. Results. Experiments demonstrated a significant improvement in key search metrics (NDCG, MRR) compared to the baseline RuBERT model. The language model trained in the study has been uploaded to the Hugging Face platform, where it is now available for open use. Conclusion. The proposed RuBERT fine-tuning method was effective for search tasks in medical RAG systems. The current limitations of the approach and directions for further research are discussed.
Authors: I. L. Kashirina, Yu. V. Starichkova, T. K. Le
Institution: MIREA - Russian Technological University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7143385366