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Related Questions
- What are the key differences between coherence and fluency in text generation, and how do they impact model interpretability?
- How do coherence and fluency metrics inform the design of more transparent and explainable text generation models?
- Can you provide examples of how coherence and fluency metrics have been used to improve the interpretability of text generation models in real-world applications?
- What are some common pitfalls to avoid when designing coherence and fluency metrics for text generation models, and how can they be mitigated?
- How do coherence and fluency metrics relate to other aspects of model interpretability, such as model understandability and controllability?
- Can you discuss the trade-offs between coherence, fluency, and other metrics such as perplexity and cross-entropy in text generation model design?
- What are some open research questions in the area of coherence and fluency metrics for text generation models, and how can they be addressed through future research?
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