Diffusion-based generative models, as first introduced by Sohl-Dickstein et al. (2015), have shown

Developing procedures for sampling higher-quality translations is therefore in high demand.

Autoformalization is the task of translating natural language materials into machine-verifiable formalisations. Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence.

Code is available at this link .

Further analysis substantiates our hypothesis that our improvement can be attributed to reduced overfitting to instruction tuning datasets. It is worth noting that we are not proposing IM as a replacement for the current instruction tuning process. Instead, our work aims to provide practical guidance for instruction tuning LMs, especially in low-resource scenarios.