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Related Questions
- What are the key performance indicators (KPIs) used by prompt engineers to measure the accuracy of a fine-tuned language model?
- How do prompt engineers evaluate the robustness of their fine-tuned language models in handling out-of-distribution data?
- What are some common metrics used to measure the interpretability of a fine-tuned language model's decision-making process?
- What is the role of human evaluation in assessing the effectiveness of a fine-tuned language model, and how is it used in conjunction with automated metrics?
- Can you explain the concept of bias in fine-tuned language models and how prompt engineers can measure and mitigate it?
- What are some techniques used by prompt engineers to evaluate the generalizability of a fine-tuned language model across different tasks and domains?
- How do prompt engineers use active learning and human-in-the-loop approaches to improve the performance and effectiveness of fine-tuned language models?
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