9 papers
Challenges in Explaining Pretrained Clinical Text Classifiers
Kristian Miok, Matej Klemen, Blaz Å krlj +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 ÄuranoviÄ, Marko Robnik Å ikonja
Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources.…
Incremental Graph Construction Enables Robust Spectral Clustering of Texts
Marko PranjiÄ, Boshko Koloski, Nada LavraÄ +2
Neighborhood graphs are a critical but often fragile step in spectral clustering of text embeddings. On realistic text datasets, standard -NN graphs can contain many disconnecte…
Large language models for folktale type automation based on motifs: Cinderella case study
TjaÅ¡a ArÄon, Marko Robnik-Å ikonja, 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 Å krlj, 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 Å kvorc, Nikola IvaÄiÄ, 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…