Papers by Marian Simko
Women Are Beautiful, Men Are Leaders: Gender Stereotypes in Machine Translation and Language Modeling (2024.findings-emnlp)
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| Challenge: | Existing datasets for genderstereotypical reasoning are limited and often limited to overly specific phenomena. |
| Approach: | They propose to use GEST to measure gender-stereotypical reasoning in language models and machine translation systems. |
| Outcome: | The proposed dataset contains 16 gender stereotypes compatible with the English language and 9 Slavic languages. |
SlovakBERT: Slovak Masked Language Model (2022.findings-emnlp)
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Matúš Pikuliak, Štefan Grivalský, Martin Konôpka, Miroslav Blšták, Martin Tamajka, Viktor Bachratý, Marian Simko, Pavol Balážik, Michal Trnka, Filip Uhlárik
| Challenge: | SlovakBERT is a new masked language model that is based on a Web-crawled corpus. |
| Approach: | They introduce a new Slovak-only transformers-based language model called SlovkBERT . they evaluate the model on several NLP tasks and establish a benchmark for Slovakia . |
| Outcome: | The proposed model achieves state-of-the-art on several NLP tasks and achieves best results . the proposed model could be used by other Slovak researchers or NLP practitioners . |
Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection (2026.eacl-long)
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Ivan Vykopal, Antonia Karamolegkou, Jaroslav Kopčan, Qiwei Peng, Tomáš Javůrek, Michal Gregor, Marian Simko
| Challenge: | Recent advances in multilingual Large Language Models have enabled powerful capabilities for cross-lingual fact-checking. |
| Approach: | They evaluate six open-source multilingual LLMs across 20 languages using a fully multilingual prompting strategy. |
| Outcome: | The proposed model performs better on high-resource languages than on low-resourced ones. |
skLEP: A Slovak General Language Understanding Benchmark (2025.findings-acl)
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Marek Suppa, Andrej Ridzik, Daniel Hládek, Tomáš Javůrek, Viktória Ondrejová, Kristína Sásiková, Martin Tamajka, Marian Simko
| Challenge: | skLEP is the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding models. |
| Approach: | They introduce a benchmark specifically designed for evaluating Slovak natural language understanding models. |
| Outcome: | The proposed benchmark covers nine tasks that span token-level, sentence-pair, document-level tasks. |
Assessing Web Search Credibility and Response Groundedness in Chat Assistants (2026.eacl-long)
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| Challenge: | Using 100 claims across five misinformation-prone topics, we assess GPT-4o, GPT-5, Perplexity, and Qwen Chat. |
| Approach: | They propose a method for evaluating assistants’ web search behavior focusing on source credibility and the groundedness of responses with respect to cited sources. |
| Outcome: | The proposed method focuses on source credibility and the groundedness of responses with respect to cited sources. |
Large Language Models for Multilingual Previously Fact-Checked Claim Detection (2025.findings-emnlp)
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| Challenge: | a new study evaluates large language models for multilingual previously fact-checked claim detection . authors assess seven LLMs across 20 languages in monolingual and cross-lingual settings . |
| Approach: | They evaluate large language models for multilingual previously fact-checked claim detection . they find they perform well for high-resource languages, struggle with low-resourced languages . |
| Outcome: | The proposed model performs well for high-resource languages, but struggle with low-resourced languages. |
Soft Language Prompts for Language Transfer (2025.naacl-long)
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| Challenge: | Cross-lingual knowledge transfer, especially between high- and low-resource languages, remains challenging in natural language processing. |
| Approach: | They propose to combine language-specific adapters and soft prompts to enhance cross-lingual transfer by parameter-efficient fine-tuning methods. |
| Outcome: | The proposed methods outperform language adapters and soft prompts in 16 languages and 10 low-resource languages. |