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Related Questions
- What are some common challenges human evaluators face when assessing the contextual nuances of text generated by language models?
- How do human evaluators account for implicit context and background knowledge when evaluating the relevance and accuracy of text generated by language models?
- What are some potential biases that human evaluators may introduce when evaluating the contextual nuances of text generated by language models?
- Can you explain the difference between explicit and implicit context in text generation, and how human evaluators consider these aspects when evaluating model performance?
- How do human evaluators balance the need for precision and recall when evaluating the contextual nuances of text generated by language models?
- What are some strategies human evaluators use to mitigate the limitations of metrics like MRR and NDCG when evaluating the contextual nuances of text generated by language models?
- Can you discuss the role of human evaluators in identifying and addressing contextual nuances that are not captured by automated metrics like MRR and NDCG?
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