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Related Questions
- What are some common biases that can be introduced during data curation in paraphrasing tasks?
- How can data preprocessing techniques, such as tokenization and stopword removal, impact the presence of biases in paraphrasing models?
- Can biased language patterns in the training data be amplified or attenuated through paraphrasing, and how can this be mitigated?
- What role do domain-specific knowledge and cultural context play in introducing or reducing biases in paraphrasing tasks?
- How can the choice of evaluation metrics, such as BLEU or ROUGE, influence the presence of biases in paraphrasing models?
- Can paraphrasing tasks perpetuate existing social biases, and if so, how can this be addressed through data curation and preprocessing?
- What are some strategies for detecting and mitigating biases in paraphrasing models, such as debiasing word embeddings or using fairness metrics?
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