3 citations · 5 across the 8 of their papers we have counts for
8 papers
DelTriC: A Novel Clustering Method with Accurate Outlier
Tomas Javurek, Michal Gregor, Sebastian Kula +1
The paper introduces DelTriC (Delaunay Triangulation Clustering), a clustering algorithm which integrates PCA/UMAP-based projection, Delaunay triangulation, and a novel back-projec…
Assessing Web Search Credibility and Response Groundedness in Chat Assistants
Ivan Vykopal, Matúš Pikuliak, Simon Ostermann +1
Chat assistants increasingly integrate web search functionality, enabling them to retrieve and cite external sources. While this promises more reliable answers, it also raises the…
Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection
Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kopčan +4
Multilingual Large Language Models (LLMs) offer powerful capabilities for cross-lingual fact-checking. However, these models often exhibit language bias, performing disproportionat…
SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
Qiwei Peng, Robert Moro, Michal Gregor +7
The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low…
Generative Large Language Models in Automated Fact-Checking: A Survey
Ivan Vykopal, Matúš Pikuliak, Simon Ostermann +1
The rapid spread of false and misleading information on online platforms poses a growing societal challenge, overwhelming the capacity of manual fact-checking and increasing the de…
Soft Language Prompts for Language Transfer
Ivan Vykopal, Simon Ostermann, Marián Šimko
Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing (NLP). This study offers insights for impr…
Beyond Image-Text Matching: Verb Understanding in Multimodal Transformers Using Guided Masking
Ivana Beňová, Jana Košecká, Michal Gregor +3
The dominant probing approaches rely on the zero-shot performance of image-text matching tasks to gain a finer-grained understanding of the representations learned by recent multim…
Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling
Matúš Pikuliak, Andrea Hrckova, Stefan Oresko +1
We present GEST -- a new manually created dataset designed to measure gender-stereotypical reasoning in language models and machine translation systems. GEST contains samples for 1…