Papers by Dominik Macko
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark (2023.emnlp-main)
Copied to clipboard
Dominik Macko, Robert Moro, Adaku Uchendu, Jason Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | MULTITuDE benchmarks lack authentic and machine-generated text in languages other than English . defining characteristic of new generation of LLMs is increased quality of text . |
| Approach: | They propose a benchmarking dataset for multilingual machine-generated text detection that compares detectors with authentic and machine-generated texts in 11 languages. |
| Outcome: | The proposed dataset compares detectors with zero-shot and fine-tuned detectors in 11 languages. |
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts (2025.acl-long)
Copied to clipboard
| Challenge: | Existing methods for detecting social-media texts are limited to the English language and longer texts are not easily recognisable by humans. |
| Approach: | They propose to use a multilingual and multi-platform dataset to compare machine-generated text detection methods in the social-media domain to compare them to human-written texts. |
| Outcome: | The proposed dataset contains 472,097 texts, of which about 58k are human-written and approximately the same amount is generated by each of 7 multilingual LLMs. |
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking (2024.acl-demos)
Copied to clipboard
| Challenge: | MGTD methods are needed in many areas, such as prevention of disinformation spreading, plagiarism, impersonation and identity theft. |
| Approach: | They propose a framework for machine-generated text detection that integrates custom methods and evaluation datasets into existing frameworks. |
| Outcome: | The proposed framework simplifies the benchmarking of machine-generated text detection methods by easy integration of custom (new) methods and evaluation datasets. |
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation (2025.acl-long)
Copied to clipboard
Aneta Zugecova, Dominik Macko, Ivan Srba, Robert Moro, Jakub Kopál, Katarína Marcinčinová, Matúš Mesarčík
| Challenge: | Recent large language models generate disinformation news articles following predefined narratives . personalization and disinformation abilities of LLMs have not been studied . |
| Approach: | They evaluate the personalization and disinformation abilities of large language models . they find personalization reduces the safety-filter activations, thus effectively functioning as a jailbreak . |
| Outcome: | The proposed model generates disinformation news articles in english with the lowest quality of personalization. |
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts (2024.acl-long)
Copied to clipboard
Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
| Challenge: | Using a computational approach, we discover that diminishing performance in text classification models is closely associated with the extent of deviation from the original author’s style. |
| Approach: | They propose to use large language models to determine whether a text retains original authorship when it undergoes numerous paraphrasing iterations. |
| Outcome: | The results suggest that authorship should be task-dependent . |
Authorship Attribution in Multilingual Machine-Generated Texts (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have reached human-like fluency and coherence, but distinguishing machine-generated text from human-written content becomes increasingly difficult. |
| Approach: | They propose a problem of multilingual authorship attribution (AA) that involves attributing texts to human or multiple LLM generators across diverse languages. |
| Outcome: | The proposed method can be adapted to multilingual settings, but still has significant limitations and challenges. |
CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for machine-generated text detection are mostly focused on English . existing methods are almost unusable for non-English languages, leaving the transferability towards these languages unexplored. |
| Approach: | They propose to use a train-language combination to compare MGT detection methods . they focus on multi-domain, multi-generator, and multilingual evaluation . |
| Outcome: | The proposed methods are the most performant in the Central European languages and resistant against obfuscation. |
Authorship Obfuscation in Multilingual Machine-Generated Text Detection (2024.findings-emnlp)
Copied to clipboard
Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
| Challenge: | Recent advances in Language Modeling have birthed Large Language Models (LLMs), which exhibit significant improvements, including the ability to generate texts easily misconstrued as humanwritten. |
| Approach: | They compare authorship obfuscation methods against machine-generated text (MGT) in 11 languages and analyze their performance against 37 well-known AO methods. |
| Outcome: | The proposed methods can cause evasion of detection in all languages, with homoglyph attacks particularly successful. |