3 papers
cs.CL2025
ReCoVeR the Target Language: Language Steering without Sacrificing Task Performance
Hannah Sterz, Fabian David Schmidt, Goran Glavaš +1
As they become increasingly multilingual, Large Language Models (LLMs) exhibit more language confusion, i.e., they tend to generate answers in a language different from the languag…
cs.CL2025
DARE: Diverse Visual Question Answering with Robustness Evaluation
Hannah Sterz, Jonas Pfeiffer, Ivan VuliÄ
Vision Language Models (VLMs) extend remarkable capabilities of text-only large language models and vision-only models, and are able to learn from and process multi-modal vision-te…
cs.CL2024
M2QA: Multi-domain Multilingual Question Answering
Leon Engländer, Hannah Sterz, Clifton Poth +3
Generalization and robustness to input variation are core desiderata of machine learning research. Language varies along several axes, most importantly, language instance (e.g. Fre…