Zobrazeno 1 - 10
of 2 227
pro vyhledávání: '"Macko, P."'
Autor:
Bouet, R., Busto, J., Cadiou, A., Charpentier, P., Charrier, D., Chapellier, M., Dastgheibi-Fard, A., Druillole, F., Hellmuth, P., Jollet, C., Kaizer, J., Kontul, I., Ray, P. Le, Gros, M., Lautridou, P., Macko, M., Meregaglia, A., Piquemal, F., Povinec, P., Roche, M.
To search for $\beta\beta0\nu$ decay with unprecedented sensitivity, the R2D2 collaboration is developing a radial time projection chamber with a fiducial mass of half a ton of 136Xe at high pressure. The various approaches implemented to eliminate t
Externí odkaz:
http://arxiv.org/abs/2411.03722
Autor:
Boža, Vladimír, Macko, Vladimír
Neural networks are often challenging to work with due to their large size and complexity. To address this, various methods aim to reduce model size by sparsifying or decomposing weight matrices, such as magnitude pruning and low-rank or block-diagon
Externí odkaz:
http://arxiv.org/abs/2409.18850
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 however mostly focused on the English language and longer texts, such as
Externí odkaz:
http://arxiv.org/abs/2406.12549
Autor:
Spiegel, Michal, Macko, Dominik
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 misuse of large language models (LLMs), the newest of which are very
Externí odkaz:
http://arxiv.org/abs/2402.13671
Autor:
Macko, Dominik, Moro, Robert, Uchendu, Adaku, Srba, Ivan, Lucas, Jason Samuel, Yamashita, Michiharu, Tripto, Nafis Irtiza, Lee, Dongwon, Simko, Jakub, Bielikova, Maria
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-generated text (MGT) detection is important to cope with such threats. H
Externí odkaz:
http://arxiv.org/abs/2401.07867
Autor:
Spiegel, Michal, Macko, Dominik
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 to annotation purposes. It is, however, also important to properly eval
Externí odkaz:
http://arxiv.org/abs/2311.12574
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 disinformation content might have dramatic consequences for societies a
Externí odkaz:
http://arxiv.org/abs/2311.08838
Autor:
Tripto, Nafis Irtiza, Venkatraman, Saranya, Macko, Dominik, Moro, Robert, Srba, Ivan, Uchendu, Adaku, Le, Thai, Lee, Dongwon
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 Theseus paradox, which ponders whether a ship remains the same when each of
Externí odkaz:
http://arxiv.org/abs/2311.08374
Autor:
Macko, Dominik, Moro, Robert, Uchendu, Adaku, Lucas, Jason Samuel, Yamashita, Michiharu, Pikuliak, Matúš, Srba, Ivan, Le, Thai, Lee, Dongwon, Simko, Jakub, Bielikova, Maria
Publikováno v:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings. This is also reflected in the available ben
Externí odkaz:
http://arxiv.org/abs/2310.13606
Constant evolution and the emergence of new cyberattacks require the development of advanced techniques for defense. This paper aims to measure the impact of a supervised filter (classifier) in network anomaly detection. We perform our experiments by
Externí odkaz:
http://arxiv.org/abs/2310.06656