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Open AccessArticle
Ten Natural Language Processing Tasks with Generative Artificial Intelligence
by
Justyna Golec
Justyna Golec
and
Tomasz Hachaj
Tomasz Hachaj
Tomasz Hachaj received an M.S. in computer science from the
Krakow University of Technology, in a a [...]
Tomasz Hachaj received an M.S. in computer science from the
Krakow University of Technology, Poland, in 2006, a Ph.D. degree in computer
science from AGH University of Science and Technology, Krakow, Poland, in 2010,
and a D.S. (habilitation) in computer science from Wrocław University of
Science and Technology, Poland, in 2017. He works in the Department of Applied
Computer Science at AGH University of Science and Technology, Kraków, Poland.
He has participated in various Polish national projects, being involved at both
the technical/research and administrative levels. He is the Principal
Investigator in the Machine Learning group in the Cosmic Ray Extremely
Distributed Observatory (CREDO). His research interest is oriented to the
development and application of deep learning, signal processing, and pattern
recognition methods in various fields.
1
Institute of Security and Computer Science, University of the National Education Commission of Krakow, 2 Podchorazych Ave, 30-084 Krakow, Poland
2
Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, Mickiewicza 30, 30-059 Krakow, Poland
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(16), 9057; https://doi.org/10.3390/app15169057 (registering DOI)
Submission received: 7 July 2025
/
Revised: 29 July 2025
/
Accepted: 13 August 2025
/
Published: 17 August 2025
Abstract
The review enumerates the predominant applications of large language models (LLMs) in natural language processing (NLP) tasks, with a particular emphasis on the years 2023 to 2025. A particular emphasis is placed on applications pertaining to information retrieval, named entity recognition, text or document classification, text summarization, machine translation, question-and-answer generation, fake news or hate speech detection, and sentiment analysis of text. Furthermore, metrics such as ROUGE, BERT, METEOR, BART, and BLEU scores are presented to evaluate the capabilities of a given language model. The following example illustrates the calculation of scores for the aforementioned metrics, utilizing sentences generated by ChatGPT 3.5, which is free and publicly available.
Share and Cite
MDPI and ACS Style
Golec, J.; Hachaj, T.
Ten Natural Language Processing Tasks with Generative Artificial Intelligence. Appl. Sci. 2025, 15, 9057.
https://doi.org/10.3390/app15169057
AMA Style
Golec J, Hachaj T.
Ten Natural Language Processing Tasks with Generative Artificial Intelligence. Applied Sciences. 2025; 15(16):9057.
https://doi.org/10.3390/app15169057
Chicago/Turabian Style
Golec, Justyna, and Tomasz Hachaj.
2025. "Ten Natural Language Processing Tasks with Generative Artificial Intelligence" Applied Sciences 15, no. 16: 9057.
https://doi.org/10.3390/app15169057
APA Style
Golec, J., & Hachaj, T.
(2025). Ten Natural Language Processing Tasks with Generative Artificial Intelligence. Applied Sciences, 15(16), 9057.
https://doi.org/10.3390/app15169057
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