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Related Questions
- What are some common limitations of using Mean Reciprocal Rank (MRR) as an evaluation metric for text generation models?
- How do noise in the relevance judgments affect the calculation of Normalized Discounted Cumulative Gain (NDCG) scores?
- What are some potential biases in MRR and NDCG metrics that can lead to inaccurate model evaluations?
- How do MRR and NDCG metrics handle cases where the ground truth is not clear or is partially missing?
- What are some common issues with using MRR and NDCG metrics for evaluating text generation models in low-resource languages?
- How do MRR and NDCG metrics account for the quality of generated text versus its relevance to the query?
- What are some techniques for mitigating the impact of evaluation metrics on the training data and model behavior?
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