125 citations · 367 across the 35 of their papers we have counts for
14 papers · 1 filter
A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models
Steffen Herbold, Florian Lemmerich
The generation of texts using Large Language Models (LLMs) is inherently uncertain, with sources of uncertainty being not only the generation of texts, but also the prompt used and…
Benevolent Dictators? On LLM Agent Behavior in Dictator Games
Andreas Einwiller, Kanishka Ghosh Dastidar, Artur Romazanov +3
In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for fairness or self-interest of study participants. In the dictator game, a simp…
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…
Linearity-based neural network compression
Silas Dobler, Florian Lemmerich
In neural network compression, most current methods reduce unnecessary parameters by measuring importance and redundancy. To augment already highly optimized existing solutions, we…
SubROC: AUC-Based Discovery of Exceptional Subgroup Performance for Binary Classifiers
Tom Siegl, Kutalmış Coşkun, Bjarne C. Hiller +3
Machine learning (ML) is increasingly employed in real-world applications like medicine or economics, thus, potentially affecting large populations. However, ML models often do not…
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…
Updating Embeddings for Dynamic Knowledge Graphs
Christopher Wewer, Florian Lemmerich, Michael Cochez
Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize informatio…
Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels
Tiago Santos, Florian Lemmerich, Denis Helic
As a tool for capturing irregular temporal dependencies (rather than resorting to binning temporal observations to construct time series), Hawkes processes with exponential decay h…
A Comparative Evaluation of Quantification Methods
Tobias Schumacher, Markus Strohmaier, Florian Lemmerich
Quantification represents the problem of estimating the distribution of class labels on unseen data. It also represents a growing research field in supervised machine learning, for…
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…
Privacy Attacks on Network Embeddings
Michael Ellers, Michael Cochez, Tobias Schumacher +2
Data ownership and data protection are increasingly important topics with ethical and legal implications, e.g., with the right to erasure established in the European General Data P…