2 citations · 4 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
Chapter 7 Review of Data-Driven Generative AI Models for Knowledge Extraction from Scientific Literature in Healthcare
Leon Kopitar, Primoz Kocbek, Lucija Gosak +1
This review examines the development of abstractive NLP-based text summarization approaches and compares them to existing techniques for extractive summarization. A brief history o…
Identifying and Decomposing Compound Ingredients in Meal Plans Using Large Language Models
Leon Kopitar, Leon Bedrac, Larissa J Strath +2
This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models…
Improving Primary Healthcare Workflow Using Extreme Summarization of Scientific Literature Based on Generative AI
Gregor Stiglic, Leon Kopitar, Lucija Gosak +5
Primary care professionals struggle to keep up to date with the latest scientific literature critical in guiding evidence-based practice related to their daily work. To help solve…