Search Results for author:
Found 5 papers, 3 papers with code
VariErr NLI: Separating Annotation Error from Human Label Variation
To fill this gap, we introduce a systematic methodology and a new dataset, VariErr (variation versus error), focusing on the NLI task in English.
"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models
The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging.
HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools
Instead, they are applied in the wild, i. e., on application-dependent text collections different from those used for the tools' training, varying, e. g., in focus, genre, style, and text type.
Donkii: Can Annotation Error Detection Methods Find Errors in Instruction-Tuning Datasets?
Our results show that the choice of the right AED method and model size is indeed crucial and derive practical recommendations for how to use AED methods to clean instruction-tuning data.
BELB: a Biomedical Entity Linking Benchmark
Biomedical entity linking (BEL) is the task of grounding entity mentions to a knowledge base.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
