Cross-lingual Code Generation Performance via Intermediate Python Task Training

Abstract

Abstract: The use of Large Language Models (LLMs) for program code generation has gained substantial attention, but their biases and limitations with non-English prompts challenge global inclusivity. This paper investigates the complexities of multilingual prompt-based code generation. Our evaluations of LLMs, including CODELLAMA and CODEGEMMA, reveal significant disparities in code quality for non-English prompts; we also demonstrate the inadequacy of simple approaches like prompt translation, bootstrapped data augmentation, and fine-tuning. To address this, we propose a zero-shot cross-lingual approac

Research Question

How does intermediate-task training on Python code generation benchmarks (e.g., HumanEval, MBPP) affect zero-shot cross-lingual performance on JavaScript code generation tasks (e.g., JS100K) compared to models fine-tuned on multilingual code datasets like CodeT5+?

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Provenance