Understanding the Multilingual and Multicultural Impact of LLMs

The research introduces RTP-LX, a unique dataset specifically targeting the detection of toxic content in 28 languages using Large Language Models (LLMs). The study emphasizes the importance of culturally sensitive and multilingual toxic content evaluation.### Key Findings from the Study

• Participatory design for dataset creation ensures inclusivity and relevancy.

• Majority of the evaluated S/LLMs show acceptable accuracy but struggle with cultural nuances like microagressions or bias, underscoring the challenge of scale in toxicity detection.

• The release of this corpus aims to enhance the safe deployment of AI systems by mitigating harmful language use.

  The Significance and Future Directions

• This work highlights the critical need for tools that can operate across cultural and linguistic boundaries to ensure ethical AI deployments.

• The comprehensive nature of the dataset enables further research on improving LLMs’ performance in culturally complex scenarios, potentially directing new developments in AI safety and bias detection.