Keywords: code generation, execution-based evaluation, test-based evaluation, language models, multi-lingual code generation benchmark, code insertion, code summarization, robustness for code, code translation, zero-shot code translation, multi-lingual, mono-lingual, language models.
Abstract: We present two new benchmarks, MBXP and Multilingual HumanEval, designed to evaluate code completion models in over 10 programming languages. These datasets are generated using a conversion framework that transpiles prompts and test cases from the original MBPP and HumanEval datasets into the corresponding data in the target language. By using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities. In addition, we use our code generation model to perform large-scale bootstrapping to obtain synthetic canonical solutions in several languages, which can be used for other code-related evaluations such as code insertion, robustness, or summarization tasks.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
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