Specialising small language models with less data

Most AI teams are exploring the possibilities of LLMs, rather than being focused on margins but soon efficiency will become important. Implementing small, specialized models to solve specific problems is an option, but is not leveraged often, because it requires gathering high volumes of human-labeled training data which are hard to acquire. To alleviate this problem, I will discuss how large language models can be used to generate synthetic data used to help tune small models on domain-specific tasks. We will focus on extractive question answering use cases where additional unstructured context can help training.