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Related Questions
- What are some common challenges when fine-tuning a language model for analogy and metaphor generation, and how can they be addressed?
- How can domain knowledge and industry-specific terminology be effectively incorporated into an LLM for analogy and metaphor generation?
- What are some strategies for handling out-of-vocabulary words and phrases when fine-tuning an LLM for a specific industry or domain?
- How can the quality of training data impact the performance of an LLM in generating analogies and metaphors, and what steps can be taken to improve data quality?
- What are some common pitfalls to avoid when fine-tuning an LLM for a specific industry or domain, such as overfitting or underfitting?
- How can the use of transfer learning and multi-task learning be leveraged to improve the performance of an LLM in generating analogies and metaphors?
- What are some metrics or evaluation methods that can be used to assess the quality and effectiveness of an LLM's analogy and metaphor generation capabilities?
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