Case context: You are building a sentiment classifier for mobile app reviews. Human annotators are nearly perfect on this task. Your model has a 2% error rate on the training set, and a 2.2% error rate on a held-out set drawn from the same source as the training data. But when you evaluate on the dev set, the error rate rises to 11%.

Question: Identify the most likely issue affecting performance on the dev set, and explain which error rates support that conclusion.

Sample answer: The most likely issue is a mismatch between the training distribution and the dev-set distribution. The model performs similarly on training data and on other examples from the same source (2% versus 2.2%), so there is no sign of a large overfitting problem to the training set. The jump to 11% on the dev set shows that the dev examples come from a different or harder distribution that the model has not learned well.

Key points:

• The main diagnosis is a distribution mismatch.
• The close 2% and 2.2% error rates show the model generalizes well within the training distribution.
• The much larger 11% dev-set error indicates the dev set differs in some important way from the data used for training and same-distribution evaluation.

Rubric: The response must identify distribution mismatch as the diagnosis and support it by comparing the small gap between training and same-distribution error with the much larger gap to dev-set error.