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Related Questions
- How can prompt engineering help mitigate representation bias in language models?
- What are some common pitfalls that can exacerbate representation bias in prompts and how can they be avoided?
- Can you provide examples of prompts that have been optimized to reduce representation bias and improve model performance?
- In what ways can prompt improvements be used to address underrepresentation of certain groups or topics in language models?
- How do you measure the effectiveness of prompt improvements in reducing representation bias in language models?
- Can you discuss the relationship between prompt engineering and data curation in addressing representation bias?
- What are some best practices for ensuring that prompt improvements do not inadvertently introduce new biases or perpetuate existing ones?
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