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Updated: Jun 7, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
[Artificial intelligence and large language models: challenges and prospects in research and medicine]
S Taratkin M1,2,3, Yu Shchelkunova K1,2,3, R Azilgareeva C1,2,3
1Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia.
Abstract:
With the development and spread of artificial intelligence, technologies based on the neural networks (for example, large language models) have attracted the most attention as promising methods for analyzing and processing data in various fields. Large language models (LLMs) are systems trained on huge amounts of text data and capable of generating answers to user queries. Examples of well-known LLMs are ChatGPT, Bing, Sparrow, BlenderBot, Bard, YandexGPT, GigaChat and others. Currently, artificial intelligence (AI) plays an important role in scientific and research work, including processing of medical data, making diagnoses, drafting scientific papers and documentation, writing articles, reviews and other academic materials. The evolution and use of large language models in various fields of medicine (and beyond) is presented in the article. In addition, the prospects for their future use, obstacles that hinder their active implementation and the importance of monitoring their use are analyzed.
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