Respectful or Toxic? Using Zero-Shot Learning with Language Models to Detect Hate Speech
Flor Miriam Plaza-del-Arco, Debora Nozza, Dirk Hovy · 2023
Hate speech detection faces two significant challenges: 1) the limited availability of labeled data and 2) the high variability of hate speech across different contexts and languages.Prompting brings a ray of hope to these challenges.It allows injecting a model with taskspecific knowledge without relying on labeled data.This paper explores zero-shot learning with prompting for hate speech detection.We investigate how well zero-shot learning can detect hate speech in 3 languages with limited labeled data.We experiment with various large language models and verbalizers on 8 benchmark datasets.Our findings highlight the impact of prompt selection on the results.They also suggest that prompting, specifically with recent large language models, can achieve performance comparable to and surpass fine-tuned models, making it a promising alternative for under-resourced languages.Our findings highlight the potential of prompting for hate speech detection and show how both the prompt and the model have a significant impact on achieving more accurate predictions in this task.