8 papers · 1 filter
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…
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…