Jan Kocoń
Adiunkt
Jan Kocoń is an Assistant Professor at the Wroclaw University of Science and Technology, where he received a Ph.D. degree in computer science (2018) and an MSc. Eng. degree (2012). He is also AI/ML Team Leader and Senior ML/NLP Data Scientist in the CLARIN-BIZ project. He has worked on natural language processing (NLP) for over a decade, especially using machine learning methods. He is the author of more than 60 scientific publications, presented at conferences such as ACL, ICDM, CoNLL, COLING, PerCOM, ICCS, KES, LREC, RANLP, and GWC. He is currently working on advanced personalized models based on deep learning in subjective tasks, such as emotion, sentiment, hate speech, or humor recognition. He also works on cross-lingual knowledge transfer and the application of language-agnostic models. He actively participated in the following projects using machine learning-based solutions: SYNAT, NEKST, CLARIN-PL, Parthenos, AZON, Sentimenti, CLARIN-BIZ, Q-Travel, and AI Tech. He lectures on an introduction to data science, the application of artificial intelligence in natural language processing, and the construction of advanced deep neural network models. Winner of HackYeah 2021 - the largest hackathon in Europe - in a task related to optimizing the construction of a power plant based on renewable energy sources for hydrogen production.
Grupy badawcze
Wykształcenie
- 2012
magister informatyki
Politechnika Wrocławska
- 2018
doktor informatyki, sztuczna inteligencja
Politechnika Wrocławska
Publikacje98
Artificial intelligence overload: a multilevel taxonomy and the path forward
P Kazienko, WM Mieleszczenko-Kowszewicz, J Kocoń, B Bajcar, J Sienkiewicz, JA Hołyst, …
IEEE 2026
Exploring the future of psychometrics from a Large Language Model perspective: A case study analysis
WM Mieleszczenko-Kowszewicz, J Bielaniewicz, J Kocoń, P Kazienko, K Kanclerz
Computers in Human Behavior Reports 22, 1–14
Breaking the illusion of reasoning in Polish LLMs: quality over quantity of thought
D Pihulski, M Langner, J Eliasz, P Kazienko, J Kocoń, T Ferdinan, …
Materiały konferencyjne
CLARIN‑PL: a user centred language technology infrastructure
M Piasecki, A Dziob-Zadworna, A Janz, J Kocoń, T Naskręt, MŁ Oleksy, …, T Walkowiak, …, K Hwaszcz
Language Resources and Evaluation 59, 4493–4528 · Cytowane przez 1
LLMSQL: Upgrading WikiSQL for the LLM Era of text-to-SQL
D Pihulski, K Charchut, V Novogrodskaia, J Kocoń
Materiały konferencyjne
Divide, cache, conquer: dichotomic prompting for efficient multi-label LLM-based classification
M Langner, JK Eliasz, EK Rudnicka, J Kocoń
Materiały konferencyjne
Architectural concepts for integrating fundamental drives and emotions into artificial intelligence
T Ferdinan, WM Mieleszczenko-Kowszewicz, J Kocoń, P Kazienko
IEEE 2025
Typology of image crises using large language models: a novel approach to crisis classification
G Chodak, AP Szczęsny, P Kazienko, O Kaszyca, MŁ Oleksy, M Kochanek, …, E Dzięcioł, T Kajdanowicz, M Piasecki, J Kocoń, …
Journal of Contingencies and Crisis Management 33(4), 1–20
Predicting stock prices with ChatGPT-annotated Reddit sentiment: hype or reality?
M Kmak, K Chmurzyński, K Matejuk, P Kotzbach, J Kocoń
Lecture notes in computer science · Cytowane przez 1
SupResDiffGAN a new approach for the super-resolution task
DA Kopeć, WM Kozłowski, M Wizerkaniuk, D Krutul, J Kocoń, MM Zięba
Lecture notes in computer science · Cytowane przez 3
Backtranslation and paraphrasing in the LLM era? Comparing data augmentation methods for emotion classification
Ł Radliński, M Guściora, J Kocoń
Materiały konferencyjne · Cytowane przez 1
AggTruth: contextual hallucination detection using aggregated attention scores in LLMs
PJ Matys, JK Eliasz, KM Kiełczyński, M Langner, T Ferdinan, J Kocoń, P Kazienko
Materiały konferencyjne
Improving LLM-Based Recommender Systems with User-Controllable Profiles
SJ Woźniak, JJ Duszenko, J Kocoń, P Kazienko
WWW 2025 · Cytowane przez 4
Integrating personalized and contextual information in fine-grained emotion recognition in text: A multi-source fusion approach with explainability
A Ngo, J Kocoń
Information Fusion · Cytowane przez 7
Fortifying NLP models against poisoning attacks: The power of personalized prediction architectures
Information Fusion 114, 1–19 · Cytowane przez 5
PolEval 2024 Task 2: Emotion and sentiment recognition
Institute of Computer Science, Polish Academy of Sciences 31–41
Small language models for emotion recognition in Polish stock market investor opinions
B Koptyra, MŁ Oleksy, E Dzięcioł, J Kocoń
Materiały konferencyjne · Cytowane przez 1
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