Measuring The Impact Of Programming Language Distribution

• URL: http://arxiv.org/abs/2302.01973v3
• Date: Wed, 24 May 2023 16:20:33 GMT
• Title: Measuring The Impact Of Programming Language Distribution
• Authors: Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta
• Abstract summary: We present the BabelCode framework for execution-based evaluation of any benchmark in any language. We present a new code translation dataset called Translating Python Programming Puzzles (TP3) We investigate if balancing the distributions of 14 languages in a training dataset improves a large language model's performance on low-resource languages.
• Score: 28.96076723773365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigations into the qualitative performance of models' memory, runtime, and individual test case results. Additionally, we present a new code translation dataset called Translating Python Programming Puzzles (TP3) from the Python Programming Puzzles (Schuster et al. 2021) benchmark that involves translating expert-level python functions to any language. With both BabelCode and the TP3 benchmark, we investigate if balancing the distributions of 14 languages in a training dataset improves a large language model's performance on low-resource languages. Training a model on a balanced corpus results in, on average, 12.34% higher $pass@k$ across all tasks and languages compared to the baseline. We find that this strategy achieves 66.48% better $pass@k$ on low-resource languages at the cost of only a 12.94% decrease to high-resource languages. In our three translation tasks, this strategy yields, on average, 30.77% better low-resource $pass@k$ while having 19.58% worse high-resource $pass@k$.

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