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cs.CL2025

On Multilingual Encoder Language Model Compression for Low-Resource Languages

Daniil Gurgurov, Michal Gregor, Josef van Genabith +1

In this paper, we combine two-step knowledge distillation, structured pruning, truncation, and vocabulary trimming for extremely compressing multilingual encoder-only language mode…

cs.CL2025

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…

cs.CL2025

Large Language Models for Multilingual Previously Fact-Checked Claim Detection

Ivan Vykopal, Matúš Pikuliak, Simon Ostermann +3

In our era of widespread false information, human fact-checkers often face the challenge of duplicating efforts when verifying claims that may have already been addressed in other…

cs.CL2025

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…

cs.CL2025

A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languages

Ivan Vykopal, Martin Hyben, Robert Moro +2

Online disinformation poses a global challenge, placing significant demands on fact-checkers who must verify claims efficiently to prevent the spread of false information. A major…

cs.CL2024

ExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread

Jake Vasilakes, Zhixue Zhao, Ivan Vykopal +3

Addressing online disinformation requires analysing narratives across languages to help fact-checkers and journalists sift through large amounts of data. The ExU project focuses on…