3 papers
cs.CL2026
LLMs Explain't: A Post-Mortem on Semantic Interpretability in Transformer Models
Alhassan Abdelhalim, Janick Edinger, Sören Laue +1
Large Language Models (LLMs) are becoming increasingly popular in pervasive computing due to their versatility and strong performance. However, despite their ubiquitous use, the ex…
cs.CL2025
Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings
Hanna Herasimchyk, Alhassan Abdelhalim, Sören Laue +1
Understanding what knowledge is implicitly encoded in deep learning models is essential for improving the interpretability of AI systems. This paper examines common methods to expl…
cs.CL2025
Automating Violence Detection and Categorization from Ancient Texts
Alhassan Abdelhalim, Michaela Regneri
Violence descriptions in literature offer valuable insights for a wide range of research in the humanities. For historians, depictions of violence are of special interest for analy…