Leveraging Zero and Few-Shot Learning for Enhanced Model Generality in Hate Speech Detection in Spanish and English

José Antonio García-Díaz, Ronghao Pan () and Rafael Valencia-García
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José Antonio García-Díaz: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain
Ronghao Pan: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain
Rafael Valencia-García: Facultad de Informática, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain

Mathematics, 2023, vol. 11, issue 24, 1-19

Abstract: Supervised training has traditionally been the cornerstone of hate speech detection models, but it often falls short when faced with unseen scenarios. Zero and few-shot learning offers an interesting alternative to traditional supervised approaches. In this paper, we explore the advantages of zero and few-shot learning over supervised training, with a particular focus on hate speech detection datasets covering different domains and levels of complexity. We evaluate the generalization capabilities of generative models such as T5, BLOOM, and Llama-2. These models have shown promise in text generation and have demonstrated the ability to learn from limited labeled data. Moreover, by evaluating their performance on both Spanish and English datasets, we gain insight into their cross-lingual applicability and versatility, thus contributing to a broader understanding of generative models in natural language processing. Our results highlight the potential of generative models to bridge the gap between data scarcity and model performance across languages and domains.

Keywords: hate speech detection; zero-shot learning; few-shot learning; fine-tuning; large language models; natural language processing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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