Exploring Large Language Models for Hate Speech Detection in Rioplatense Spanish
This work addresses hate speech detection for a specific Spanish dialect, offering incremental insights into LLM performance compared to existing methods.
The paper tackled hate speech detection in Rioplatense Spanish by evaluating large language models like ChatGPT 3.5, Mixtral, and Aya against a fine-tuned BERT classifier, finding that while LLMs had lower precision, they were more sensitive to nuanced cases such as homophobic/transphobic hate speech.
Hate speech detection deals with many language variants, slang, slurs, expression modalities, and cultural nuances. This outlines the importance of working with specific corpora, when addressing hate speech within the scope of Natural Language Processing, recently revolutionized by the irruption of Large Language Models. This work presents a brief analysis of the performance of large language models in the detection of Hate Speech for Rioplatense Spanish. We performed classification experiments leveraging chain-of-thought reasoning with ChatGPT 3.5, Mixtral, and Aya, comparing their results with those of a state-of-the-art BERT classifier. These experiments outline that, even if large language models show a lower precision compared to the fine-tuned BERT classifier and, in some cases, they find hard-to-get slurs or colloquialisms, they still are sensitive to highly nuanced cases (particularly, homophobic/transphobic hate speech). We make our code and models publicly available for future research.