1 citations · 1 across the 12 of their papers we have counts for
12 papers
Greater accessibility can amplify discrimination in generative AI
Carolin Holtermann, Minh Duc Bui, Kaitlyn Zhou +3
Hundreds of millions of people rely on large language models (LLMs) for education, work, and even healthcare. Yet these models are known to reproduce and amplify social biases pres…
SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation
Carolin Holtermann, Florian Schneider, Anne Lauscher
Text-to-image (T2I) models are increasingly employed by users worldwide. However, prior research has pointed to the high sensitivity of T2I towards particular input languages - whe…
TempViz: On the Evaluation of Temporal Knowledge in Text-to-Image Models
Carolin Holtermann, Nina Krebs, Anne Lauscher
Time alters the visual appearance of entities in our world, like objects, places, and animals. Thus, for accurately generating contextually-relevant images, knowledge and reasoning…
Large Language Models Discriminate Against Speakers of German Dialects
Minh Duc Bui, Carolin Holtermann, Valentin Hofmann +2
Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Ad…
Around the World in 24 Hours: Probing LLM Knowledge of Time and Place
Carolin Holtermann, Paul Röttger, Anne Lauscher
Reasoning over time and space is essential for understanding our world. However, the abilities of language models in this area are largely unexplored as previous work has tested th…
GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge Benchmarking
Florian Schneider, Carolin Holtermann, Chris Biemann +1
Large Vision-Language Models (LVLMs) have recently gained attention due to their distinctive performance and broad applicability. While it has been previously shown that their effi…
Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model
Gregor Geigle, Florian Schneider, Carolin Holtermann +4
Most Large Vision-Language Models (LVLMs) to date are trained predominantly on English data, which makes them struggle to understand non-English input and fail to generate output i…
Why do LLaVA Vision-Language Models Reply to Images in English?
Musashi Hinck, Carolin Holtermann, Matthew Lyle Olson +6
We uncover a surprising multilingual bias occurring in a popular class of multimodal vision-language models (VLMs). Including an image in the query to a LLaVA-style VLM significant…
Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ
Carolin Holtermann, Paul Röttger, Timm Dill +1
Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. However, most LLMs today, and open LLMs in particular, are often intended…
What the Weight?! A Unified Framework for Zero-Shot Knowledge Composition
Carolin Holtermann, Markus Frohmann, Navid Rekabsaz +1
The knowledge encapsulated in a model is the core factor determining its final performance on downstream tasks. Much research in NLP has focused on efficient methods for storing an…
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale
Markus Frohmann, Carolin Holtermann, Shahed Masoudian +2
Multi-task learning (MTL) has shown considerable practical benefits, particularly when using language models (LMs). While this is commonly achieved by learning tasks under a jo…
Fair and Argumentative Language Modeling for Computational Argumentation
Carolin Holtermann, Anne Lauscher, Simone Paolo Ponzetto
Although much work in NLP has focused on measuring and mitigating stereotypical bias in semantic spaces, research addressing bias in computational argumentation is still in its inf…