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Related Questions
- What are the key considerations for designing inclusive and diverse training datasets in NLP models?
- How can prompt engineering techniques be used to mitigate bias and ensure fairness in language models?
- What are some strategies for testing and evaluating the fairness of NLP models in real-world scenarios?
- Can you explain the concept of 'data provenance' and its importance in ensuring fairness in NLP models?
- How can prompt engineering be used to address issues of representation and underrepresentation in NLP models?
- What role do you think human evaluation plays in ensuring fairness and accuracy in NLP models?
- Can you discuss the trade-offs between model performance and fairness in NLP, and how prompt engineering can help balance these competing priorities?
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