2025W

• Experiment Design and Execution / 194.192 / VU

• TU Wien at SemEval-2024 Task 6: Unifying Model-Agnostic and Model-Aware Techniques for Hallucination Detection / Arzt, V., Azarbeik, M. M., Lasy, I., Kerl, T., & Recski, G. (2024). TU Wien at SemEval-2024 Task 6: Unifying Model-Agnostic and Model-Aware Techniques for Hallucination Detection. In A. K. Ojha, A. S. Dogruöz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, & A. Rosá (Eds.), Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) (pp. 1183–1196). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.semeval-1.173

• Evaluation of Sparse Autoencoder-based Refusal Features in LLMs: Dataset-dependence study / Kerl, T. (2025). Evaluation of Sparse Autoencoder-based Refusal Features in LLMs: Dataset-dependence study [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2025.130185
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