Besides the choice of phrasing, the generation
source of prompts is another factor in prompt de-
sign. Gudibande et al. (2023) raise questions on
fine-tuning a weaker language model on outputs of
a stronger model and find that the imitation model
can only adapt to mimic the stronger model’s style
but not its functionality, indicating the failure of
closing the performance gap between open- and
close-sourced through imitation instruction tuning.
Similarly, (Song et al., 2023) also observe that
human-designed data can outperform synthetically
generated data from GPT-4 (OpenAI, 2023) to a
large extent.
DataManagementForLargeLanguageModels-ASurvey