Database Normalization via Dual-LLM Self-Refinement
This addresses the time-consuming and error-prone task of database normalization for data engineers, though it appears incremental as it applies existing LLM capabilities to a specific domain.
The paper tackles the problem of automating database normalization, which is typically manual and error-prone, by introducing Miffie, a framework that uses dual large language models for self-refinement, achieving high accuracy in normalizing complex schemas.
Database normalization is crucial to preserving data integrity. However, it is time-consuming and error-prone, as it is typically performed manually by data engineers. To this end, we present Miffie, a database normalization framework that leverages the capability of large language models. Miffie enables automated data normalization without human effort while preserving high accuracy. The core of Miffie is a dual-model self-refinement architecture that combines the best-performing models for normalized schema generation and verification, respectively. The generation module eliminates anomalies based on the feedback of the verification module until the output schema satisfies the requirement for normalization. We also carefully design task-specific zero-shot prompts to guide the models for achieving both high accuracy and cost efficiency. Experimental results show that Miffie can normalize complex database schemas while maintaining high accuracy.