Publications

• A. Barrón-Cedeño, F. Alam, T. Chakraborty, T. Elsayed, P. Nakov, P. Przybyła, J. M. Struß, F. Haouari, M. Hasanain, F. Ruggeri, X. Song, R. Suwaileh, “The CLEF-2024 CheckThat! Lab: Check-Worthiness, Subjectivity, Persuasion, Roles, Authorities, and Adversarial Robustness,” in Proceedings of the 46th European Conference on Information Retrieval (ECIR 2024), Glasgow, UK, 2024.[paper][preprint]
• P. Przybyła, H. Saggion, “ERINIA: Evaluating the Robustness of Non-Credible Text Identification by Anticipating Adversarial Actions,” in Proceedings of the Workshop on NLP applied to Misinformation co-located with 39th International Conference of the Spanish Society for Natural Language Processing (SEPLN 2023), Jaén, Spain, 2023. [paper]
• P. Przybyła, N. Duran-Silva, S. Egea-Gómez, “I've Seen Things You Machines Wouldn't Believe: Measuring Content Predictability to Identify Automatically-Generated Text,” in Proceedings of the 5th Workshop on Iberian Languages Evaluation Forum (IberLEF 2023), Jaén, Spain, 2023. [paper]
• P. Przybyła, A. Shvets, H. Saggion, “BODEGA: Benchmark for Adversarial Example Generation in Credibility Assessment,” Manuscript arXiv:2303.08032 [cs.CL], 2023.[preprint]

Resources

BODEGA framework helps to evaluate the robustness of text classifiers, i.e. their ability to maintain the correct prediction for test examples that were modified by a malicious attacker. BODEGA is using tasks related to the detection of misinformation and aims to simulate the real usecase of social media platforms employing ML classifiers for content filtering. The framework is openly available on GitHub.

Autext is a software package for building ML models performing identification of machine-generated text. The solution is based on measuring the predictability if the textual content as has received an award for the best performance in the AuTexTification shared task at IberLEF 2023. The source code is available for download from GitHub.