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Related Questions
- What are some common pitfalls in language model training that can lead to biased outputs?
- How can prompt engineering techniques be used to detect and mitigate implicit bias in language models?
- What are some best practices for designing unbiased prompts for language models?
- Can you explain the concept of 'adversarial testing' in the context of prompt engineering and bias mitigation?
- How can prompt engineering techniques be used to address bias in specific domains, such as healthcare or finance?
- What role do you think data curation plays in reducing bias in language models, and how can prompt engineering techniques support this process?
- Can you discuss the trade-offs between achieving high accuracy and avoiding bias in language model outputs, and how prompt engineering can help navigate these trade-offs?
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