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Related Questions
- What are some common pitfalls in detecting bias in language models, and how can they be mitigated?
- How do contextualized embeddings, such as those generated by BERT or RoBERTa, help to capture nuanced language patterns and reduce bias in language models?
- Can you explain the concept of 'bias amplification' in language models, and how contextualized embeddings can help to mitigate this issue?
- What role do contextualized embeddings play in improving the fairness and transparency of language models, and how can they be used to detect and mitigate bias?
- How do contextualized embeddings help to address the challenge of 'data bias' in language models, and what are some strategies for mitigating this issue?
- Can you discuss the trade-offs between model performance and fairness in language models, and how contextualized embeddings can help to balance these competing goals?
- What are some best practices for using contextualized embeddings to detect and mitigate bias in language models, and how can these practices be applied in real-world applications?
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