Effective cross-lingual LLM evaluation with Amazon Bedrock

This article explores effective cross-lingual large language model (LLM) evaluation techniques using Amazon Bedrock, focusing on how to assess AI responses consistently across multiple languages.

• Used Indonesian SEA-MTBench dataset with 116 evaluation records
• Conducted evaluations using both human annotators and LLM-as-a-judge approaches
• Compared evaluation results using English and Indonesian judge prompts
• Found strong correlation between LLM judges and human evaluations across languages
• Demonstrated that English evaluation prompts can effectively judge non-English responses

Key findings show that LLM-as-a-judge is a practical, scalable method for multilingual AI model evaluation, with human evaluations still serving as an essential baseline. Amazon Bedrock's evaluation features simplify this complex process.