426 citations · 433 across the 11 of their papers we have counts for
4 papers · 2 filters
Exploration of Augmentation Strategies in Multi-modal Retrieval-Augmented Generation for the Biomedical Domain: A Case Study Evaluating Question Answering in Glycobiology
Primož Kocbek, Azra Frkatović-Hodžić, Dora Lalić +3
Multi-modal retrieval-augmented generation (MM-RAG) promises grounded biomedical QA, but it is unclear when to (i) convert figures/tables into text versus (ii) use optical characte…
UM_FHS at the CLEF 2025 SimpleText Track: Comparing No-Context and Fine-Tune Approaches for GPT-4.1 Models in Sentence and Document-Level Text Simplification
Primoz Kocbek, Gregor Stiglic
This work describes our submission to the CLEF 2025 SimpleText track Task 1, addressing both sentenceand document-level simplification of scientific texts. The methodology centered…
Plain language adaptations of biomedical text using LLMs: Comparision of evaluation metrics
Primoz Kocbek, Leon Kopitar, Gregor Stiglic
This study investigated the application of Large Language Models (LLMs) for simplifying biomedical texts to enhance health literacy. Using a public dataset, which included plain la…
UM_FHS at TREC 2024 PLABA: Exploration of Fine-tuning and AI agent approach for plain language adaptations of biomedical text
Primoz Kocbek, Leon Kopitar, Zhihong Zhang +3
This paper describes our submissions to the TREC 2024 PLABA track with the aim to simplify biomedical abstracts for a K8-level audience (13-14 years old students). We tested three…