Multi-lingual Evaluation of Code Generation ModelsDownload PDF

Published: 01 Feb 2023, 19:18, Last Modified: 01 Feb 2023, 19:18ICLR 2023 notable top 25%Readers: Everyone
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 MBXP, an execution-based code completion benchmark in 10+ programming languages. This collection of datasets is generated by our conversion framework that translates prompts and test cases from the original MBPP dataset to the corresponding data in a target language. Based on this benchmark, we are able to evaluate code generation models in a multi-lingual fashion, and in particular discover generalization ability of language models on out-of-domain languages, advantages of large multi-lingual models over mono-lingual, benefits of few-shot prompting, 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. These solutions can be used for other code-related evaluations such as insertion-based, summarization, or code translation tasks where we demonstrate results and release as part of our benchmark.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning
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