62 citations · 199 across the 49 of their papers we have counts for
55 papers
Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
Leon Weber-Genzel, Robert Litschko, Ekaterina Artemova +1
Instruction tuning has become an integral part of training pipelines for Large Language Models (LLMs) and has been shown to yield strong performance gains. In an orthogonal line of…
Uncertainty in Natural Language Generation: From Theory to Applications
Joris Baan, Nico Daheim, Evgenia Ilia +7
Recent advances of powerful Language Models have allowed Natural Language Generation (NLG) to emerge as an important technology that can not only perform traditional tasks like sum…
Findings of the VarDial Evaluation Campaign 2023
Noëmi Aepli, Çağrı Çöltekin, Rob Van Der Goot +7
This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2023. The campaign is part of the tenth workshop on Natural Language Proce…
ActiveAED: A Human in the Loop Improves Annotation Error Detection
Leon Weber, Barbara Plank
Manually annotated datasets are crucial for training and evaluating Natural Language Processing models. However, recent work has discovered that even widely-used benchmark datasets…
Boosting Zero-shot Cross-lingual Retrieval by Training on Artificially Code-Switched Data
Robert Litschko, Ekaterina Artemova, Barbara Plank
Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In…
ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain
Mike Zhang, Rob van der Goot, Barbara Plank
The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related…