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Related Questions
- What are some common pitfalls or biases that can occur when evaluating language model outputs based on prompt design, and how can they be mitigated?
- How can prompt designers ensure that language models are not perpetuating existing biases or stereotypes in their outputs?
- What are some best practices for evaluating the fairness and transparency of language model outputs, and how can they be incorporated into prompt design?
- Can you explain the concept of 'prompt leakage' and how it can impact the reliability of language model outputs?
- How can prompt designers use techniques such as debiasing and data augmentation to improve the fairness and accuracy of language model outputs?
- What are some common pitfalls of using language models for tasks that require nuanced understanding or common sense, and how can they be addressed?
- How can prompt designers use techniques such as active learning and human evaluation to improve the quality and reliability of language model outputs?
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