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20202026
most citedKnowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

93 citations · 216 across the 33 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models

Luka Debevc, Nishan Chatterjee, Antoine Doucet +2

Large Language Models are increasingly deployed as information intermediaries, yet measuring their political behavior remains fragile because questionnaire results mix model dispos…

cs.LG2026

KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models

Boshko Koloski, Xiangjian Jiang, Senja Pollak +3

Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks. Yet in niche domains, where data…

cs.CL2026

Environmental, Social and Governance Sentiment Analysis on Slovene News: A Novel Dataset and Models

Paula Dodig, Boshko Koloski, Katarina Sitar Šuštar +2

Environmental, Social, and Governance (ESG) considerations are increasingly integral to assessing corporate performance, reputation, and long-term sustainability. Yet, reliable ESG…

cs.CL2026

Multilingual Cognitive Impairment Detection in the Era of Foundation Models

Damar Hoogland, Boshko Koloski, Jaya Caporusso +5

We evaluate cognitive impairment (CI) classification from transcripts of speech in English, Slovene, and Korean. We compare zero-shot large language models (LLMs) used as direct cl…

cs.LG2026

Incremental Graph Construction Enables Robust Spectral Clustering of Texts

Marko Pranjić, Boshko Koloski, Nada Lavrač +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…