no code implementations • 20 Feb 2024 • Branislav Pecher, Ivan Srba, Maria Bielikova
When performance variance is taken into consideration, the number of required labels increases on average by $100 - 200\%$ and even up to $1500\%$ in specific cases.
no code implementations • 20 Feb 2024 • Branislav Pecher, Ivan Srba, Maria Bielikova
To measure the true effects of an individual randomness factor, our method mitigates the effects of other factors and observes how the performance varies across multiple runs.
no code implementations • 5 Feb 2024 • Branislav Pecher, Ivan Srba, Maria Bielikova, Joaquin Vanschoren
In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the limited number of samples used to train a model have a significant impact on the overall success.
1 code implementation • 12 Jan 2024 • Jan Cegin, Branislav Pecher, Jakub Simko, Ivan Srba, Maria Bielikova, Peter Brusilovsky
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models.
no code implementations • 2 Dec 2023 • Branislav Pecher, Ivan Srba, Maria Bielikova
Recently, this area started to attract a research attention and the number of relevant studies is continuously growing.
1 code implementation • 24 Apr 2023 • Timo Hromadka, Timotej Smolen, Tomas Remis, Branislav Pecher, Ivan Srba
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection.
1 code implementation • 18 Oct 2022 • Ivan Srba, Robert Moro, Matus Tomlein, Branislav Pecher, Jakub Simko, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Adrian Gavornik, Maria Bielikova
We also observe a sudden decrease of misinformation filter bubble effect when misinformation debunking videos are watched after misinformation promoting videos, suggesting a strong contextuality of recommendations.
1 code implementation • 26 Apr 2022 • Ivan Srba, Branislav Pecher, Matus Tomlein, Robert Moro, Elena Stefancova, Jakub Simko, Maria Bielikova
It also contains 573 manually and more than 51k automatically labelled mappings between claims and articles.
1 code implementation • 25 Mar 2022 • Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, Maria Bielikova
We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics).