HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization
This addresses a critical gap for researchers and developers working on multilingual AI systems by providing a comprehensive evaluation platform, though it is incremental as it builds on existing benchmark concepts.
The authors tackled the lack of benchmarks for evaluating multilingual large language models in code generation across diverse natural languages, by introducing HumanEval-XL, a benchmark with 22,080 prompts connecting 23 natural languages and 12 programming languages.
Large language models (LLMs) have made significant progress in generating codes from textual prompts. However, existing benchmarks have mainly concentrated on translating English prompts to multilingual codes or have been constrained to very limited natural languages (NLs). These benchmarks have overlooked the vast landscape of massively multilingual NL to multilingual code, leaving a critical gap in the evaluation of multilingual LLMs. In response, we introduce HumanEval-XL, a massively multilingual code generation benchmark specifically crafted to address this deficiency. HumanEval-XL establishes connections between 23 NLs and 12 programming languages (PLs), and comprises of a collection of 22,080 prompts with an average of 8.33 test cases. By ensuring parallel data across multiple NLs and PLs, HumanEval-XL offers a comprehensive evaluation platform for multilingual LLMs, allowing the assessment of the understanding of different NLs. Our work serves as a pioneering step towards filling the void in evaluating NL generalization in the area of multilingual code generation. We make our evaluation code and data publicly available at \url{https://github.com/FloatAI/humaneval-xl}.