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Related Questions
- What are some common pitfalls to avoid when designing prompts for large language models to mitigate intersectional bias?
- How can I use prompt priming to ensure that my model is not perpetuating stereotypes or biases?
- What are some techniques for making language models more inclusive and representative of diverse populations?
- How can I design prompts that account for the nuances of intersectional bias and promote more accurate and equitable outcomes?
- What are some best practices for testing and evaluating the effectiveness of prompt priming in reducing bias?
- Can you provide examples of successful prompt engineering strategies that have been used to mitigate intersectional bias in AI systems?
- What are some ways to design prompts that take into account the complexities of intersectional identity and avoid reinforcing harmful stereotypes?
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