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Related Questions
- What are the common biases present in automated metrics used to evaluate text generation models?
- How do metrics like ROUGE and BLEU score address the nuances of human language, and are they sufficient for evaluating domain knowledge?
- Can automated metrics accurately capture the context and common sense required for domain-specific knowledge?
- What are the consequences of relying solely on automated metrics to evaluate text generation models, and how do they impact model development?
- How do human evaluators and domain experts contribute to the evaluation process, and what benefits do their inputs provide?
- What are the trade-offs between using automated metrics and human evaluation in assessing domain knowledge in text generation models?
- Can machine learning algorithms be designed to learn from human feedback and adapt to the nuances of domain-specific knowledge?
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