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Related Questions
- How do you ensure that your training data is diverse and representative of different cultures and perspectives to reduce bias?
- What strategies do you use to detect and mitigate bias in your training data, particularly when it comes to sensitive topics like race and gender?
- Can you walk me through your process for annotating and curating data to address potential biases in the model's training data?
- How do you balance the need for diverse training data with the risk of introducing new biases or stereotypes?
- What role do you think human evaluators play in identifying and addressing biases in the model's performance on sensitive topics?
- Can you discuss the trade-offs between using more data versus more diverse data in training a language model, particularly when it comes to sensitive topics?
- How do you measure the effectiveness of your data curation and annotation efforts in reducing bias in the model's performance on sensitive topics?
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