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 self-generated explanation


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

Tang, Zilu, Agarwal, Mayank, Shypula, Alex, Wang, Bailin, Wijaya, Derry, Chen, Jie, Kim, Yoon

arXiv.org Artificial Intelligence

This work explores the use of self-generated natural language explanations as an intermediate step for code-to-code translation with language models. Across three types of explanations and 19 programming languages constructed from the MultiPL-E dataset, we find the explanations to be particularly effective in the zero-shot case, improving performance by 12% on average. Improvements with natural language explanations are particularly pronounced on difficult programs. We release our dataset, code, and canonical solutions in all 19 languages.