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Related Questions
- How can you validate the diversity of the test dataset to ensure it reflects the complexities of the domain?
- What strategies can be employed to capture the subtleties of language used in the domain, such as idioms, colloquialisms, and jargon?
- How can you assess the test dataset's ability to generalize to unseen data and real-world scenarios?
- What techniques can be used to identify and mitigate biases in the test dataset, ensuring it is representative of the domain?
- How can you ensure the test dataset includes a range of perspectives, including those from underrepresented groups?
- What methods can be used to evaluate the test dataset's coverage of the domain's key concepts, terminology, and nuances?
- How can you validate the test dataset's ability to capture the context-dependent nature of language in the domain?
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