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Related Questions
- Can biases in pre-trained language models affect the accuracy of summaries generated by summarization models?
- Do summarization models inherit biases from the underlying pre-trained language models, even if the summarization model's training data is diverse and representative?
- How can the use of biased pre-trained language models impact the fairness and reliability of summaries produced by summarization models?
- Can the summarization model's output be affected by the biases present in the pre-trained language model, even if the summarization model's training data is not biased?
- Are there any strategies to mitigate the introduction of biases from pre-trained language models in summarization models?
- Can biases in pre-trained language models lead to unfair or inaccurate summaries, especially in applications where the stakes are high?
- How can the development and evaluation of summarization models be designed to account for the potential biases present in pre-trained language models?
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