On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation

• URL: http://arxiv.org/abs/2409.03946v1
• Date: Fri, 6 Sep 2024 00:02:09 GMT
• Title: On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generation
• Authors: Banooqa Banday, Kowshik Thopalli, Tanzima Z. Islam, Jayaraman J. Thiagarajan,
• Abstract summary: We explore three prompt construction protocols: Expert-guided, LLM-guided, and Novel-Mapping. We find that context-enriched prompts lead to significantly improved data generation quality and training efficiency.
• Score: 16.79923685316516
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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