1 citations · 1 across the 4 of their papers we have counts for
6 papers
Taxonomy-Driven Analysis of Open-Source AI Risk Mitigation Tools
Afreen Alam, Evgenija Popchanovska, Ana Gjorgjevikj +4
Rapid adoption of large language models (LLMs) in enterprise settings has introduced operational, security, and governance risks. As generative AI applications move from pilot to p…
Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs
Denica Kjorvezir, Marko DjukanoviÄ, Ana Gjorgjevikj +2
Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming. We propose a graph-based prompt selection framework that models each benchm…
On the Robustness of Multilingual Text Embedding Rankings Across Learning Tasks, Languages, and Benchmark Datasets
Ana Gjorgjevikj, Barbara KorouÅ¡iÄ Seljak, Tome Eftimov
Large-scale multilingual text embedding models play crucial role in both research and industry, yet their behavior in language-specific, multi-task settings remains insufficiently…
Beyond Fine-Tuning: Robust Food Entity Linking under Ontology Drift with FoodOntoRAG
Jan Drole, Ana Gjorgjevikj, Barbara Korouši'c Seljak +1
Standardizing food terms from product labels and menus into ontology concepts is a prerequisite for trustworthy dietary assessment and safety reporting. The dominant approach to Na…
When AI Fails, What Works? A Data-Driven Taxonomy of Real-World AI Risk Mitigation Strategies
Evgenija Popchanovska, Ana Gjorgjevikj, Maryan Rizinski +3
Large language models (LLMs) are increasingly embedded in high-stakes workflows, where failures propagate beyond isolated model errors into systemic breakdowns that can lead to leg…
FoodSEM: Large Language Model Specialized in Food Named-Entity Linking
Ana Gjorgjevikj, Matej Martinc, Gjorgjina Cenikj +3
This paper introduces FoodSEM, a state-of-the-art fine-tuned open-source large language model (LLM) for named-entity linking (NEL) to food-related ontologies. To the best of our kn…