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This work explores the use of self-generated natural language explanations asan intermediate step for code-to-code translation with language models. Acrossthree types of explanations and 19 programming languages constructed from theMultiPL-E dataset, we find the explanations to be particularly effective in thezero-shot case, improving performance by 12% on average. Improvements withnatural language explanations are particularly pronounced on difficultprograms. We release our dataset, code, and canonical solutions in all 19languages.

Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations