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Related Questions
- What are some common sources of bias in fine-tuning large language models and how can they be identified?
- Can you explain the concept of fairness in machine learning and how it relates to prompt engineering?
- What strategies can prompt engineers use to mitigate bias in fine-tuning, such as data augmentation or adversarial training?
- How can prompt engineers ensure that their fine-tuning approach does not perpetuate existing biases, such as those related to demographics or language usage?
- What are some best practices for validating the fairness and bias of a fine-tuned model before deploying it in production?
- Can you discuss the role of data preprocessing and cleaning in mitigating bias in fine-tuning, and provide some tips for effective data preparation?
- How can prompt engineers balance the trade-offs between model performance, bias, and fairness when fine-tuning a model, and what are some decision-making frameworks that can guide this process?
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