Türkçe Finansal RAG Uygulamalari için Gömme Modellerinin Karşilaştirilmasi

ÇETİN U., Colakgil I. E.

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Turkey, 25 - 28 June 2025, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu66497.2025.11111893
  • City: İstanbul
  • Country: Turkey
  • Keywords: Anahtar Kelimeler - NLP, Embedding, Finance, RAG
  • Galatasaray University Affiliated: Yes

Abstract

In this study, embedding models used in the retrieval phase, which play a critical role in Retrieval-Augmented Generation (RAG) systems, are compared on financial texts. The performances of open and closed source models are analyzed and their advantages, disadvantages, and usage scenarios are evaluated. In addition, important factors such as security and copyright risks of open source models and access restrictions of closed source models are discussed. Our study presents findings to improve the accuracy, reliability, and efficiency of RAG systems in the financial domain. In the financial sector, where fast access to accurate information is critical, the effects of embedding model selection are examined in detail and conclusions are drawn to guide future research and industrial applications.