The RTP-LX project introduces a new, extensive dataset to evaluate the effectiveness of small and large language models in recognizing and handling toxic content across various languages. This research emphasizes the importance of culturally nuanced detection mechanisms and highlights the limitations of current models in handling contextual subtleties. Insights include:

• Cross-lingual Toxicity Detection: The development of a new dataset designed for a culturally-sensitive, multilingual evaluation of toxicity.
• Challenges in Contextual Understanding: Identification of model shortcomings in recognizing subtle-yet-harmful content, such as microaggressions.
• Enhanced Language Models: Exploration into modifying existing models to better handle diverse and culturally-varied expressions.

This study shines a light on the urgent need for improvements in how language models handle sensitive content, ensuring both accuracy and respect for multicultural contexts.