  Large language models (LLMs) have been recently leveraged as training data
generators for various natural language processing (NLP) tasks. While previous
research has explored different approaches to training models using generated
data, they generally rely on simple class-conditional prompts, which may limit
the diversity of the generated data and inherit systematic biases of LLM. Thus,
we investigate training data generation with diversely attributed prompts
(e.g., specifying attributes like length and style), which have the potential
to yield diverse and attributed generated data. Our investigation focuses on
datasets with high cardinality and diverse domains, wherein we demonstrate that
attributed prompts outperform simple class-conditional prompts in terms of the
resulting model's performance. Additionally, we present a comprehensive
empirical study on data generation encompassing vital aspects like bias,
diversity, and efficiency, and highlight three key observations: firstly,
synthetic datasets generated by simple prompts exhibit significant biases, such
as regional bias; secondly, attribute diversity plays a pivotal role in
enhancing model performance; lastly, attributed prompts achieve the performance
of simple class-conditional prompts while utilizing only 5\% of the querying
cost of ChatGPT associated with the latter. The data and code are available on
\url{https://github.com/yueyu1030/AttrPrompt}.
