Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
Culturally-Adapted Red-Teaming Across East and Southeast Asian Contexts: A Methodological and Comparative Analysis
TL;DR
Existing multilingual safety evaluations for large language models (LLMs) often rely on direct translation, which overlooks cultural nuances. This study developed culturally-adapted (CA) datasets for Korean, Japanese, Thai, and Khmer, comparing them to direct translation (DT) datasets.
✦ Why It Matters
Engineers should incorporate culturally-adapted datasets to enhance the safety evaluation of multilingual LLMs.
Key Takeaways
- Use culturally-adapted prompts for better LLM safety evaluations.
- Recognize the limitations of direct translation in multilingual contexts.
- Evaluate LLMs with culturally relevant benchmarks for accurate risk assessment.
- Consider cultural nuances when developing AI safety protocols.
How It Works
The study constructs datasets by matching culturally-adapted prompts with direct translations, allowing for a comparative analysis of LLM performance across different languages. This approach ensures that the evaluation reflects real-world cultural contexts, rather than merely translating language.