23 citations · 40 across the 7 of their papers we have counts for
7 papers
AuAu: A Benchmark for Auditing Authoritarian Alignment in Large Language Models
Andreas Einwiller, Max Klabunde, Florian Lemmerich
The worldwide rise of authoritarianism and the growing role of Large Language Models (LLMs) in users' everyday lives raise the question of whether specific models exhibit or promot…
Revisiting the Relation Between Robustness and Universality
M. Klabunde, L. Caspari, F. Lemmerich
The modified universality hypothesis proposed by Jones et al. (2022) suggests that adversarially robust models trained for a given task are highly similar. We revisit the hypothesi…
ReSi: A Comprehensive Benchmark for Representational Similarity Measures
Max Klabunde, Tassilo Wald, Tobias Schumacher +3
Measuring the similarity of different representations of neural architectures is a fundamental task and an open research challenge for the machine learning community. This paper pr…
Towards Measuring Representational Similarity of Large Language Models
Max Klabunde, Mehdi Ben Amor, Michael Granitzer +1
Understanding the similarity of the numerous released large language models (LLMs) has many uses, e.g., simplifying model selection, detecting illegal model reuse, and advancing ou…
Similarity of Neural Network Models: A Survey of Functional and Representational Measures
Max Klabunde, Tobias Schumacher, Markus Strohmaier +1
Measuring similarity of neural networks to understand and improve their behavior has become an issue of great importance and research interest. In this survey, we provide a compreh…
On the Prediction Instability of Graph Neural Networks
Max Klabunde, Florian Lemmerich
Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems…
The Effects of Randomness on the Stability of Node Embeddings
Tobias Schumacher, Hinrikus Wolf, Martin Ritzert +6
We systematically evaluate the (in-)stability of state-of-the-art node embedding algorithms due to randomness, i.e., the random variation of their outcomes given identical algorith…