Multilingual HateCheck

Multilingual HateCheck focuses on developing robust and reliable methods for detecting hate speech across multiple languages, addressing the limitations of traditional evaluation metrics that often mask biases and systematic errors in hate speech detection models. Current research emphasizes creating functional tests, like those in the Multilingual HateCheck (MHC) framework, to diagnose specific model weaknesses by evaluating performance on carefully crafted test cases, often leveraging large language models for test generation and incorporating sociocultural knowledge for improved accuracy, particularly in low-resource languages. This work is crucial for building more effective hate speech detection systems, improving online safety, and advancing the understanding of bias and fairness in natural language processing.