2 citations · 4 across the 14 of their papers we have counts for
6 papers · 1 filter
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…