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Related Questions
- What are the key factors to consider when designing a well-crafted prompt for a language model?
- How can I use metrics such as F1 score, precision, and recall to evaluate the accuracy of a language model's responses?
- What are some common pitfalls to avoid when crafting a prompt to ensure the model generates relevant and accurate responses?
- Can you explain the concept of prompt engineering and its role in optimizing language model performance?
- How can I use human evaluation methods, such as crowd sourcing or expert review, to assess the quality of a language model's responses?
- What are some techniques for fine-tuning a language model to improve its performance on specific tasks or domains?
- How can I use automated metrics, such as perplexity and ROUGE score, to evaluate the quality of a language model's responses?
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