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Related Questions
- What are some strategies for collecting and incorporating diverse and representative data into large language models?
- How can explicit prompt design be used to mitigate bias in language models, and what are some best practices for achieving this?
- What are some common sources of bias in language models and how can they be addressed through prompt engineering and data curation?
- Can you explain the concept of 'adversarial testing' in the context of prompt design and how it can be used to detect and mitigate bias in language models?
- What role does domain knowledge play in prompt design, and how can it be used to create more accurate and unbiased language models?
- How can explicit prompt design be used to improve the interpretability of language models and make them more transparent?
- What are some techniques for auditing and evaluating the fairness and bias of language models, and how can explicit prompt design be used to improve their performance in this regard?
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