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Why End-to-End Models Need Labeled Structured Targets
Question: Explain what it means for an end-to-end system to predict structured outputs, give a few examples, and state what kind of training data is needed for successful learning.
Sample answer: Predicting structured outputs means the model produces results that are more complex than a single numeric value. Instead of outputting one score, the system may generate a full sentence, a sequence of labels, an object list, or another structured result. To train such a model well, you need correctly matched input-output examples for the task.
Key points:
- Structured outputs are more complex than a single number.
- Examples include sentences, label sequences, and object lists.
- Training requires the right labeled input-output pairs.
Rubric: The response must explain that structured outputs are more complex than a single scalar, provide at least two valid examples, and identify the need for correctly labeled input-output pairs.
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