Faculty of Computer Science and Engineering
Permanent URI for this communityhttps://repository.ukim.mk/handle/20.500.12188/5
The Faculty of Computer Science and Engineering (FCSE) within UKIM is the largest and most prestigious faculty in the field of computer science and technologies in Macedonia, and among the largest
faculties in that field in the region.
The FCSE teaching staff consists of 50 professors and 30 associates. These include many “best in field” personnel, such as the most referenced scientists in Macedonia and the most influential professors in the ICT industry in the Republic of Macedonia.
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Item type:Publication, Optimizing document retrieval using massive text embeddings and LLM prompt engineering(Springer Science and Business Media LLC, 2026-04-14); ; Kampel, MartinBackground The rapid expansion of digital data poses a unique challenge for retrieving relevant and insightful information efficiently. In particular, the increasing volume of scientific publications has made literature reviews time-consuming. The emergence of large language models (LLMs) offers new opportunities to streamline this process. Methods This paper explores the use of generative artificial intelligence (GenAI) for query reformulation and evaluates the performance of nine massive text embedding models, varying in size and fine-tuning strategies, in the context of document retrieval. We apply multiple prompt engineering techniques to evaluate the ability of LLMs to generate effective queries, comparing them with human-crafted queries. These are used to retrieve documents utilizing nine embedding models. The evaluation is across five datasets using metrics such as recall, average precision, and rank-based measures. Results Results show that embedding models fine-tuned for semantic similarity consistently outperform general-purpose models, with UAE Large proving most robust across diverse domains. Furthermore, queries generated using zero-shot and few-shot prompting techniques often surpass the performance of human-formulated queries. Conclusion These findings highlight the value of integrating LLMs and massive text embeddings to reduce manual effort in literature reviews. GenAI provides a reliable starting point for query formulation, with human input reserved for refinement when needed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Scoping Review of Technology Enabled Healthcare Integration - Towards Sustainable Care(IEEE, 2025-06-16); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Benchmarking OpenAI's APIs and Large Language Models for Repeatable, Efficient Question Answering Across Multiple Documents(Polish Information Processing Society, 2024-10-23) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Colonoscopy image analysis for polyp detection: A systematic review of existing approaches and opportunities(Elsevier BV, 2025); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A low-cost device-based data approach to Eight Hop Test(Elsevier BV, 2025) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Virtual reality as a learning tool: Evaluating the use and effectiveness of simulation laboratories in educational settings(Elsevier BV, 2025-01-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comparative analysis of PPO and SAC algorithms for energy optimization with country-level energy consumption insights(Elsevier BV, 2025-12); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Internet of Things Ontologies for Well-Being, Aging and Health: A Scoping Literature Review(MDPI AG, 2025-01-20); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Air pollution data: A dataset gathered through a crowd sensing platform(Elsevier BV, 2025-08); ; Herzog, Michael A. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An analytical review of optimization techniques in information retrieval for enhanced decision support(Elsevier BV, 2025-12);