3 citations · 3 across the 1 of their papers we have counts for
6 papers · 1 filter
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…
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 KopÄan +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…
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…
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…