2 citations · 3 across the 6 of their papers we have counts for
3 papers · 1 filter
DARE: Diverse Visual Question Answering with Robustness Evaluation
Hannah Sterz, Jonas Pfeiffer, Ivan Vulić
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…
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…
Scaling Sparse Fine-Tuning to Large Language Models
Alan Ansell, Ivan Vulić, Hannah Sterz +2
Large Language Models (LLMs) are difficult to fully fine-tune (e.g., with instructions or human feedback) due to their sheer number of parameters. A family of parameter-efficient s…