Explain-then-translate: an analysis on improving program translation with self-generated explanations
Zilu Tang, Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Tanti Wijaya, Jie Chen, Yoon Kim · 2023
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 (Cassano et al., 2022), 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.1 *: indicates statically typed language (vs.dynamically