We designed 3 evaluation sets: (1) Base Set (1,264 samples) held out from the simulated data. (2) Augmented Set (1,280 samples) built by adding two out-of-distribution symptoms, with corresponding dialogue contents and queries, to the Base Set (“bleeding” and “cold”, which never appeared in training data). (3) Real-World Set (944 samples) manually delineated from the the symptom checking portions (approximately 4 hours) of real-world dialogues, and annotated as evaluation samples. 
 Question: How do they select instances to their hold-out test set?
1264 instances from simulated data, 1280 instances by adding two out-of-distribution symptoms and 944 instances manually delineated from the symptom checking portions of real-word dialogues