Artificial data synthesis is a technique used to address data mismatch by artificially modifying training data to better reflect the development and test sets. For example, if a speech recognition model struggles with background noise, developers can synthesize training examples by adding recorded car noise to clean audio, efficiently creating a large dataset that matches the target distribution.