68 citations · 217 across the 13 of their papers we have counts for
13 papers
Distributed Sparse Interventions in Language Models
Maximilian S. Ernst, Lorenz Linhardt, Aaron Peikert +1
Language models perform a wide range of tasks at varying levels of abstraction with the capacity to flexibly infer tasks from context, execute multiple tasks simultaneously, and se…
Beyond the TESSERACT:Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers
Theo Chow, Mario D'Onghia, Lorenz Linhardt +4
The reliability of machine learning critically depends on dataset quality. While machine learning applied to computer vision and natural language processing benefits from high-qual…
Cat, Rat, Meow: On the Alignment of Language Model and Human Term-Similarity Judgments
Lorenz Linhardt, Tom Neuhäuser, Lenka Tětková +1
Small and mid-sized generative language models have gained increasing attention. Their size and availability make them amenable to being analyzed at a behavioral as well as a repre…
Latent Diffusion U-Net Representations Contain Positional Embeddings and Anomalies
Jonas Loos, Lorenz Linhardt
Diffusion models have demonstrated remarkable capabilities in synthesizing realistic images, spurring interest in using their representations for various downstream tasks. To bette…
Objective drives the consistency of representational similarity across datasets
Laure Ciernik, Lorenz Linhardt, Marco Morik +3
The Platonic Representation Hypothesis claims that recent foundation models are converging to a shared representation space as a function of their downstream task performance, irre…
Connecting Concept Convexity and Human-Machine Alignment in Deep Neural Networks
Teresa Dorszewski, Lenka Tětková, Lorenz Linhardt +1
Understanding how neural networks align with human cognitive processes is a crucial step toward developing more interpretable and reliable AI systems. Motivated by theories of huma…