A model’s training examples reflect choices about collection, selection, and representation. For a supervised task, labels also define the distinctions the model is being asked to learn.
Consider whether the examples cover the situations in which the system will be used. A collection can be large while leaving important cases underrepresented. Inconsistent labels can make the intended task unclear even when the inputs themselves are plentiful.
When evaluating a system, ask how the task and data relate to the real use case. The amount of data is one part of the story; its relevance, quality, and coverage are others. Those questions help connect model performance with the work it is expected to support.
Picture this situation.
Consider a collection of sample labels that covers only easy cases. An unfamiliar input may expose a boundary the examples never explained.
A second way to look.
A small trial should have a clear stopping point. Decide which uncertainty the tool can help explore and what observation would answer the next question.
- Ask which situations the examples cover.
- Look for a clear labeling rule.
- Connect the training task with the intended use.
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Related background to continue exploring this subject.
Google: an introduction to language models NIST: AI risk management framework