7 citations · 8 across the 5 of their papers we have counts for
5 papers · 1 filter
MSTS: A Multimodal Safety Test Suite for Vision-Language Models
Paul Röttger, Giuseppe Attanasio, Felix Friedrich +19
Vision-language models (VLMs), which process image and text inputs, are increasingly integrated into chat assistants and other consumer AI applications. Without proper safeguards,…
Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset
Janis Goldzycher, Paul Röttger, Gerold Schneider
Hate speech detection models are only as good as the data they are trained on. Datasets sourced from social media suffer from systematic gaps and biases, leading to unreliable mode…
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data
Janis Goldzycher, Moritz Preisig, Chantal Amrhein +1
Most research on hate speech detection has focused on English where a sizeable amount of labeled training data is available. However, to expand hate speech detection into more lang…
CL-UZH at SemEval-2023 Task 10: Sexism Detection through Incremental Fine-Tuning and Multi-Task Learning with Label Descriptions
Janis Goldzycher
The widespread popularity of social media has led to an increase in hateful, abusive, and sexist language, motivating methods for the automatic detection of such phenomena. The goa…
Hypothesis Engineering for Zero-Shot Hate Speech Detection
Janis Goldzycher, Gerold Schneider
Standard approaches to hate speech detection rely on sufficient available hate speech annotations. Extending previous work that repurposes natural language inference (NLI) models f…