>> Data Augmentation with Human Input: Interactive User Interfaces for Efficient Knowledge Capture

Machine learning models trained to perform specific tasks, such as generating documents for a specific team, require a significant amount of data to capture the full range of possible inputs and outputs. However, in many cases, there is not enough data available to train a model for a specific use case. Additionally, using a generic model will only produce generic results. Yet, we still want to utilize the capabilities of generic models.

To overcome this challenge, human input can be used to augment the available data and capture variability. This typically involves having an expert in the field label the data, which also captures their knowledge.

Interactive user interfaces (IUIs) can be used to make the knowledge capture process more efficient and fast. IUIs can reduce the possibility of error and eliminate the need for experts to keep track of texts and documents.