Self-correction for OOD generalization

In this work, we aim to study how the self-correction mechanisms aid OOD (out-of-distribution) generalization in both multimodal and language-only models. Reasoning based methods like self-refine and STaR have helped to improve the correction capacity of the language models; however there have been no studies quantifying the reasoning improvement to help OOD generalization of these models. Initial results, show an improvement of 1.6%-2% on an OOD dataset where the model is finetuned using either self-refinement or STaR on an ID (in-distribution) dataset.