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Related Questions
- What are the common pitfalls to avoid when designing contextual evaluation metrics for model bias and fairness in prompt engineering?
- How can prompt engineers ensure that their evaluation metrics are comprehensive and capture both explicit and implicit biases in language models?
- What are some key factors to consider when selecting a fairness metric for a specific prompt engineering task?
- Can you explain the difference between group fairness and individual fairness in the context of prompt engineering, and how to evaluate them?
- How can prompt engineers balance the trade-off between model performance and fairness in their evaluation metrics?
- What role do contextual factors, such as cultural background and personal experiences, play in evaluating model bias and fairness in prompt engineering?
- How can prompt engineers use adversarial testing to identify and mitigate biases in their language models?
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