Papers by Ivan Vykopal
Small Models, Big Impact: Efficient Corpus and Graph-Based Adaptation of Small Multilingual Language Models for Low-Resource Languages (2025.acl-srw)
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| Challenge: | Low-resource languages (LRLs) face significant challenges in natural language processing due to limited data. |
| Approach: | They evaluate adapter-based methods for adapting mLMs to low-resource languages . they use unstructured text and structured knowledge from ConceptNet to evaluate adapters . |
| Outcome: | The proposed methods outperform large language models and LLaMA-3 and deepSeek-R1 models on low training data. |
Multilingual Previously Fact-Checked Claim Retrieval (2023.emnlp-main)
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Matúš Pikuliak, Ivan Srba, Robert Moro, Timo Hromadka, Timotej Smoleň, Martin Melišek, Ivan Vykopal, Jakub Simko, Juraj Podroužek, Maria Bielikova
| Challenge: | Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. |
| Approach: | They propose a multilingual dataset for previously fact-checked claim retrieval using social media posts and 206k fact- checks in 39 languages written by professional fact- checkers. |
| Outcome: | The proposed method improves on the previously unsupervised method and shows that a multilingual dataset has its complexities and needs to be carefully interpreted. |
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. |
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. |