7 citations · 7 across the 8 of their papers we have counts for
6 papers · 1 filter
Challenges in Explaining Pretrained Clinical Text Classifiers
Kristian Miok, Matej Klemen, Blaz Škrlj +1
Explaining the predictions of neural models in clinical NLP remains a significant challenge, especially for complex tasks involving long, unstructured medical texts. While post-hoc…
QFS-Composer: Query-focused summarization pipeline for less resourced languages
Vuk Đuranović, Marko Robnik Šikonja
Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources.…
Large language models for folktale type automation based on motifs: Cinderella case study
Tjaša Arčon, Marko Robnik-Šikonja, Polona Tratnik
Artificial intelligence approaches are being adapted to many research areas, including digital humanities. We built a methodology for large-scale analyses in folkloristics. Using m…
TT-XAI: Trustworthy Clinical Text Explanations via Keyword Distillation and LLM Reasoning
Kristian Miok, Blaz Škrlj, Daniela Zaharie +1
Clinical language models often struggle to provide trustworthy predictions and explanations when applied to lengthy, unstructured electronic health records (EHRs). This work introd…
Real-time News Story Identification
Tadej Škvorc, Nikola Ivačič, Sebastjan Hribar +1
To improve the reading experience, many news sites organize news into topical collections, called stories. In this work, we present an approach for implementing real-time story ide…
Solving Word-Sense Disambiguation and Word-Sense Induction with Dictionary Examples
Tadej Škvorc, Marko Robnik-Šikonja
Many less-resourced languages struggle with a lack of large, task-specific datasets that are required for solving relevant tasks with modern transformer-based large language models…