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 goal: Does intermediate-task training with code-focused benchmarks (e.g., HumanEval, MBPP) improve zero-shot cross-lingual code generation performance on benchmarks like XCodeEval or Multilingual CodeGen? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.8/10.

This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.8/10.