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Related Questions
- How do human evaluators ensure that text summarization models are not perpetuating existing biases in the data they have been trained on?
- What are some common pitfalls that human evaluators should be aware of when assessing the bias of text summarization models?
- Can human evaluators use specific metrics or evaluation criteria to identify bias in text summarization models?
- How do human evaluators balance the need to assess bias in text summarization models with the need to evaluate their overall performance and accuracy?
- What role do human evaluators play in identifying and addressing potential biases in the data used to train text summarization models?
- Are there any specific techniques or methods that human evaluators can use to detect and mitigate bias in text summarization models?
- How do human evaluators ensure that their evaluation of bias in text summarization models is transparent and reproducible?
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