Method: Fine-tuned
Method
In general, to eke out maximum performance on a task, you'll need to fine-tune a custom model. However, fine-tuning a model using explanations may take thousands of example explanations, which are costly to write.

In 2022, Eric Zelikman and Yuhuai Wu et al. published a clever procedure for using a few-shot prompt to generate a dataset of explanations that could be used to fine-tune a model. The idea is to use a few-shot prompt to generate candidate explanations, and only keep the explanations that produce the correct answer. Then, to get additional explanations for some of the incorrect answers, retry the few-shot prompt but with correct answers given as part of the question. The authors called their procedure STaR (Self-taught Reasoner):