10 citations · 10 across the 13 of their papers we have counts for
15 papers
Agents Are Systems, Not Models: Rethinking Agentic Evaluation
Luis Wiedmann, Leander Girrbach, Cordelia Schmid +1
Agent evaluations increasingly go beyond a single success rate, reporting metrics such as cost, consistency, and robustness. Yet they typically treat the agent itself as fixed. In…
APM: Evaluating Style Personalization in LLMs with Arbitrary Preference Mappings
Philipp Spohn, Leander Girrbach, Zeynep Akata
Typical LLM responses tend to follow a default style, even though users often have distinct preferences regarding tone, verbosity, and formality that they do not explicitly state i…
Do LLMs Experience an Internal Polylogue? Investigating Reasoning through the Lens of Personas
Nils A. Herrmann, Leander Girrbach, Kirill Bykov +1
Recent work shows that large language models (LLMs) encode behavioral traits ("personas") as linear directions in activation space, often called "persona vectors". Prior work has u…
Are Reasoning LLMs Robust to Interventions on Their Chain-of-Thought?
Alexander von Recum, Leander Girrbach, Zeynep Akata
Reasoning LLMs (RLLMs) generate step-by-step chains of thought (CoTs) before giving an answer, which improves performance on complex tasks and makes reasoning more transparent. But…
A Systematic Study of In-the-Wild Model Merging for Large Language Models
Oğuz Kağan Hitit, Leander Girrbach, Zeynep Akata
Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving…
Sparse Autoencoders are Topic Models
Leander Girrbach, Zeynep Akata
Sparse autoencoders (SAEs) are used to analyze embeddings, but their role and practical value are debated. We propose a new perspective on SAEs by demonstrating that they can be na…
Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith +2
Vision-language models trained on large-scale multimodal datasets show strong demographic biases, but the role of training data in producing these biases remains unclear. A major b…
Reference-Free Rating of LLM Responses via Latent Information
Leander Girrbach, Chi-Ping Su, Tankred Saanum +3
How reliable are single-response LLM-as-a-judge ratings without references, and can we obtain fine-grained, deterministic scores in this setting? We study the common practice of as…
SUB: Benchmarking CBM Generalization via Synthetic Attribute Substitutions
Jessica Bader, Leander Girrbach, Stephan Alaniz +1
Concept Bottleneck Models (CBMs) and other concept-based interpretable models show great promise for making AI applications more transparent, which is essential in fields like medi…
Align-then-Unlearn: Embedding Alignment for LLM Unlearning
Philipp Spohn, Leander Girrbach, Jessica Bader +1
As large language models (LLMs) are trained on massive datasets, they have raised significant privacy and ethical concerns due to their potential to inadvertently retain sensitive…
A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith +1
With the increasing use of image generation technology, understanding its social biases, including gender bias, is essential. This paper presents a large-scale study on gender bias…
DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation
Massimo Bini, Leander Girrbach, Zeynep Akata
Parameter-Efficient FineTuning (PEFT) methods have recently gained significant popularity thanks to the widespread availability of large-scale pretrained models. These methods allo…
Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)
Leander Girrbach, Stephan Alaniz, Yiran Huang +2
Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-lan…
Addressing caveats of neural persistence with deep graph persistence
Leander Girrbach, Anders Christensen, Ole Winther +2
Neural Persistence is a prominent measure for quantifying neural network complexity, proposed in the emerging field of topological data analysis in deep learning. In this work, how…
Word Segmentation and Morphological Parsing for Sanskrit
Jingwen Li, Leander Girrbach
We describe our participation in the Word Segmentation and Morphological Parsing (WSMP) for Sanskrit hackathon. We approach the word segmentation task as a sequence labelling task…