27 citations · 38 across the 19 of their papers we have counts for
18 papers · 1 filter
mdok-style at SemEval-2026 Task 10: Finetuning LLMs for Conspiracy Detection
Dominik Macko
SemEval-2026 Task 10 is focused on conspiracy detection. Specifically, the goal is to detect whether a Reddit comment expresses a conspiracy belief. Our submitted mdok-style system…
mdok-style at SemEval-2026 Task 9: Finetuning LLMs for Multilingual Polarization Detection
Dominik Macko, Alok Debnath, Jakub Simko
SemEval-2026 Task 9 is focused on multilingual polarization detection. Specifically, it covers the identification of multilingual, multicultural and multievent polarization along t…
Interpretable Predictability-Based AI Text Detection: A Replication Study
Adam Skurla, Dominik Macko, Jakub Simko
This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts. Exact replication was not possible becau…
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Jason Lucas, Matt Murtagh-White, Adaku Uchendu +6
Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguis…
Evaluation of Multilingual LLMs Personalized Text Generation Capabilities Targeting Groups and Social-Media Platforms
Dominik Macko
Capabilities of large language models to generate multilingual coherent text have continuously enhanced in recent years, which opens concerns about their potential misuse. Previous…
CEAID: Benchmark of Multilingual Machine-Generated Text Detection Methods for Central European Languages
Dominik Macko, Jakub Kopal
Machine-generated text detection, as an important task, is predominantly focused on English in research. This makes the existing detectors almost unusable for non-English languages…
PerQ: Efficient Evaluation of Multilingual Text Personalization Quality
Dominik Macko, Andrew Pulver
Since no metrics are available to evaluate specific aspects of a text, such as its personalization quality, the researchers often rely solely on large language models to meta-evalu…
Authorship Attribution in Multilingual Machine-Generated Texts
Lucio La Cava, Dominik Macko, Róbert Móro +2
As Large Language Models (LLMs) have reached human-like fluency and coherence, distinguishing machine-generated text (MGT) from human-written content becomes increasingly difficult…
mdok of KInIT: Robustly Fine-tuned LLM for Binary and Multiclass AI-Generated Text Detection
Dominik Macko
The large language models (LLMs) are able to generate high-quality texts in multiple languages. Such texts are often not recognizable by humans as generated, and therefore present…
Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectors
Dominik Macko, Robert Moro, Ivan Srba
Since the proliferation of LLMs, there have been concerns about their misuse for harmful content creation and spreading. Recent studies justify such fears, providing evidence of LL…
Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
Dominik Macko, Aashish Anantha Ramakrishnan, Jason Samuel Lucas +4
Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While huma…
Evaluation of LLM Vulnerabilities to Being Misused for Personalized Disinformation Generation
Aneta Zugecova, Dominik Macko, Ivan Srba +4
The capabilities of recent large language models (LLMs) to generate high-quality content indistinguishable by humans from human-written texts raises many concerns regarding their m…
MultiSocial: Multilingual Benchmark of Machine-Generated Text Detection of Social-Media Texts
Dominik Macko, Jakub Kopal, Robert Moro +1
Recent LLMs are able to generate high-quality multilingual texts, indistinguishable for humans from authentic human-written ones. Research in machine-generated text detection is ho…
KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
Michal Spiegel, Dominik Macko
SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection. Such a detection is important for preventing a potential…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
IMGTB: A Framework for Machine-Generated Text Detection Benchmarking
Michal Spiegel, Dominik Macko
In the era of large language models generating high quality texts, it is a necessity to develop methods for detection of machine-generated text to avoid harmful use or simply due t…
Disinformation Capabilities of Large Language Models
Ivan Vykopal, Matúš Pikuliak, Ivan Srba +3
Automated disinformation generation is often listed as an important risk associated with large language models (LLMs). The theoretical ability to flood the information space with d…
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko +5
In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of These…