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Related Questions
- What are some limitations of using word frequency as a metric for evaluating language models?
- How do contextual factors, such as cultural background and personal experience, impact the accuracy of metrics for human language?
- What are some potential pitfalls of relying solely on automated metrics, such as F1 score or BLEU score, to evaluate language model performance?
- How can metrics be designed to capture the subtleties of human language, such as idioms, sarcasm, and figurative language?
- What role do human evaluators play in the development and validation of metrics for language models?
- How do metrics for language models compare to those for other types of machine learning models, such as image or speech recognition?
- What are some strategies for ensuring that metrics for language models are fair and unbiased, particularly in multilingual or multicultural settings?
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