On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation
<|reference_start|>On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation: LLM-based data generation for real-world tabular data can be challenged by the lack of sufficient semantic context in feature names used to describe columns. We hypothesize that enriching prompts with domain-specific insights can improve both the quality and efficiency of data generation. To test this hypothesis, we explore three prompt construction protocols: Expert-guided, LLM-guided, and Novel-Mapping. Through empirical studies with the recently proposed GReaT framework, we find that context-enriched prompts lead to significantly improved data generation quality and training efficiency.<|reference_end|>
@article{banday2024on,
    title={On The Role of Prompt Construction In Enhancing Efficacy and Efficiency
  of LLM-Based Tabular Data Generation},
    author={Banooqa Banday, Kowshik Thopalli, Tanzima Z. Islam, and Jayaraman J.
  Thiagarajan},
    journal={arXiv preprint arXiv:2409.03946},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03946},
    primaryClass={cs.CL}
}