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Related Questions
- What are some common pitfalls of using perplexity as an evaluation metric for text generation models?
- How can the lack of contextual understanding in automatic metrics like BLEU and ROUGE lead to biased evaluations?
- What are some challenges associated with using human evaluation as an evaluation metric for text generation models?
- How can the overreliance on automatic metrics like fluency and grammaticality lead to a lack of understanding of the model's overall quality?
- What are some ways to address the issue of evaluation metrics being model-dependent, rather than task-dependent?
- How can the use of evaluation metrics that prioritize fluency over content lead to models generating low-quality content?
- What are some strategies for using a combination of automatic and human evaluation metrics to get a more comprehensive understanding of a text generation model's performance?
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