Building mHumanEval: Lessons from Creating a 204-Language Benchmark
Creating a massively multilingual benchmark wasn't just about collecting code samples. It was about understanding how programming languages transcend natural language barriers, and how LLMs handle this complexity. In this post, I share the technical challenges, surprising findings, and lessons learned from building mHumanEval—a comprehensive benchmark spanning 204 natural languages and 25 programming languages with over 836,400 prompts...
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