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Related Questions
- How does the quality of the training data affect the emergence of bias in pre-trained language models during fine-tuning?
- Can you explain the concept of bias propagation and how it relates to the fine-tuning process of pre-trained language models?
- What are some common data quality issues that can lead to biased pre-trained language models, and how can they be addressed?
- How does the fine-tuning process amplify or mitigate the existing biases in pre-trained language models, and what are the implications for downstream applications?
- Can you discuss the role of data curation and preprocessing in mitigating bias in pre-trained language models during fine-tuning?
- What are some strategies for detecting and mitigating bias in pre-trained language models during fine-tuning, and how can they be implemented in practice?
- How does the relationship between data quality and bias in pre-trained language models during fine-tuning affect the overall performance and reliability of natural language processing applications?
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