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Related Questions
- What are some effective methods for annotating and categorizing metaphors in a large dataset?
- How can you ensure the diversity and representativeness of the metaphors in the dataset?
- What techniques can be used to evaluate the quality and complexity of the metaphors in the dataset?
- Can you explain the importance of context in understanding and generating metaphors, and how can it be incorporated into the dataset?
- How can you balance the need for a large dataset with the need for high-quality, unique metaphors?
- What role can human evaluation and feedback play in refining the dataset and improving the LLM's performance?
- Are there any specific challenges or limitations to consider when working with metaphors in a large language model, and how can they be addressed?
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