2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
Adapters: A Unified Library for Parameter-Efficient and Modular Transfer Learning
Clifton Poth, Hannah Sterz, Indraneil Paul +7
We introduce Adapters, an open-source library that unifies parameter-efficient and modular transfer learning in large language models. By integrating 10 diverse adapter methods int…
UKP-SQUARE: An Online Platform for Question Answering Research
Tim Baumgärtner, Kexin Wang, Rachneet Sachdeva +10
Recent advances in NLP and information retrieval have given rise to a diverse set of question answering tasks that are of different formats (e.g., extractive, abstractive), require…