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Multi-lingual evaluation of code generation models

By Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng LI, Yuchen Tian, Ming Tan, Wasi Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, Ramesh Nallapati, Baishakhi Ray, Parminder Bhatia, Sudipta Sengupta, Dan Roth, Bing Xiang
2023
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We present HumanEvalX and MBXP, execution-based code completion benchmarks in 10+ programming languages. These datasets are generated by our conversion framework that transpiles prompts and test cases from original datasets (HumanEval and MBPP) to the corresponding data in a target language. Based on these benchmarks, we are able to evaluate code generation models in a multilingual fashion, and in particular, discover generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over monolingual, the ability of few-shot prompting to teach the model a new language, 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 besides function completion such as insertion-based, summarization, or code translation tasks.
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