Researchers at Brown University Explore Zero-Shot Cross-Lingual Generalization of Preference Tuning in Detoxifying LLMs
What is the role of RLHF and RLAIF in multilingual LLM safety?
RLHF and RLAIF play crucial roles in multilingual LLM safety by mitigating toxicity in non-English languages. Cross-lingual generalization of RLHF and RLAIF has shown promise in reducing toxicity levels in LLM generations across various languages. These techniques demonstrate zero-shot cross-lingual generalization, offering efficient multilingual toxicity mitigation and addressing a critical challenge in LLM safety across diverse linguistic contexts.
What are the main challenges in multilingual LLM toxicity mitigation?
The main challenges in multilingual LLM toxicity mitigation are the lack of sufficient annotated datasets across languages, the resource-intensive nature of existing solutions, and the need for effective cross-lingual generalization techniques2. Current approaches often rely on translated English data, which may not fully address the complexities of multilingual toxicity.
How do researchers address LLM toxicity in non-English languages?
Researchers address LLM toxicity in non-English languages by using techniques like cross-lingual generalization of Reinforcement Learning with Human Feedback (RLHF) or AI Feedback (RLAIF), and translating toxic and non-toxic data from English to target languages. A recent study shows that Direct Preference Optimization (DPO) with only English training data can significantly reduce toxicity levels in LLM generations across different languages, demonstrating zero-shot cross-lingual generalization.