Emphasizing the necessity of a robust evaluation set, it’s crucial to quantify model performance rather than relying on subjective feelings. Generating examples with large language models can streamline the annotation process, but caution is needed to ensure the generated data is representative of real-world scenarios. The conversation highlights the balance between efficiency and accuracy in training models.
