  Our research demonstrates the significant benefits of using fine-tuning with
explanations to enhance the performance of language models. Unlike prompting,
which maintains the model's parameters, fine-tuning allows the model to learn
and update its parameters during a training phase. In this study, we applied
fine-tuning to various sized language models using data that contained
explanations of the output rather than merely presenting the answers. We found
that even smaller language models with as few as 60 million parameters
benefited substantially from this approach. Interestingly, our results
indicated that the detailed explanations were more beneficial to smaller models
than larger ones, with the latter gaining nearly the same advantage from any
form of explanation, irrespective of its length. Additionally, we demonstrate
that the inclusion of explanations enables the models to solve tasks that they
were not able to solve without explanations. Lastly, we argue that despite the
challenging nature of adding explanations, samples that contain explanations
not only reduce the volume of data required for training but also promote a
more effective generalization by the model. In essence, our findings suggest
that fine-tuning with explanations significantly bolsters the performance of
large language models.
