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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…
Evaluating Metalinguistic Knowledge in Large Language Models across the World's Languages
Tjaša Arčon, Matej Klemen, Marko Robnik-Šikonja +1
LLMs are routinely evaluated on language use, yet their explicit knowledge about linguistic structure remains poorly understood. Existing linguistic benchmarks focus on narrow phen…
Towards Corpus-Grounded Agentic LLMs for Multilingual Grammatical Analysis
Matej Klemen, Tjaša Arčon, Luka Terčon +2
Empirical grammar research has become increasingly data-driven, but the systematic analysis of annotated corpora still requires substantial methodological and technical effort. We…
Neural spell-checker: Beyond words with synthetic data generation
Matej Klemen, Martin Božič, Špela Arhar Holdt +1
Spell-checkers are valuable tools that enhance communication by identifying misspelled words in written texts. Recent improvements in deep learning, and in particular in large lang…
Code-mixed Sentiment and Hate-speech Prediction
Anjali Yadav, Tanya Garg, Matej Klemen +3
Code-mixed discourse combines multiple languages in a single text. It is commonly used in informal discourse in countries with several official languages, but also in many other co…