Enhancing Low-Resource Code Generation via SALT on MultiPL-E Compared to Parallel Corpus Training

Abstract

Abstract: Large language models have demonstrated the ability to generate both natural language and programming language text. Such models open up the possibility of multi-language code generation: could code generation models generalize knowledge from one language to another? Although contemporary code generation models can generate semantically correct Python code, little is known about their abilities with other languages. We propose MultiPL-E, a system for translating unit test-driven code generation benchmarks to new languages. We create the first massively multilingual code generation benchmark by

Research Question

Does the SALT approach enhance code generation performance in low-resource programming languages when evaluated on the MultiPL-E benchmark compared to models trained with parallel corpora?

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