HaYo: Repurposing DiaSafety Dataset for Dialogue Safety Evaluation in Hausa and Yoruba

Abstract

Research efforts aimed at detecting unsafe dialogues have resulted in creation of benchmark datasets and models for evaluation. The benchmarks mostly exist in English and other high resourced languages. In order to address the challenge of unavailability of dialogue safety evaluation dataset in Hausa and Yorùbá, we repurporse DiaSafety dataset to develop HaYo dataset, by providing contextualised human annotation of dialogues in DiaSafety. We provide dialogues in Hausa and Yorùbá, obtained by human translation of dialogues in the DiaSafety dataset, to raters who are native speakers. The dialogues are annotated as Unsafe or Safe. We evaluate seven models with moderation, conversational or multilingual capabilities in terms of F1 Score. Using McNemar test, we observe that the predictions of GPT-4.1 and Gemma-3-12b-it on HaYo are statistically significant at p < 0.05. In our evaluation with instructions in English, we observe lower F1 scores in six out of the seven models, comparing the performance on DiaSafety and HaYo labels. The model predictions were inconsistent with the labels in the HaYo dataset when instructions and dialogues were provided in Hausa and Yorùbá. Compared to providing instructions in English, the issues range from responses in unspecified languages to underperformance in terms of F1 score. We plan to release the HaYo dataset to the public to promote dialogue safety research, especially in under-resourced languages.

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