Show Me How It's Done: The Role of Explanations in Fine-Tuning Language Models

Understand

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.