Database Normalization via Dual-LLM Self-Refinement

2026-06-08 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Expert, long

Summary

Miffie is a novel database normalization framework designed to automate the typically manual and error-prone process of preserving data integrity. It employs a dual-model self-refinement architecture, utilizing GPT-4 for schema generation and o1-mini for verification, to achieve high accuracy without human intervention. The framework iteratively refines generated schemas based on feedback from the verification module, typically converging within three attempts. Miffie also incorporates carefully designed task-specific zero-shot prompts, which demonstrate comparable or superior accuracy to few-shot methods while significantly improving cost efficiency by minimizing token usage. Experimental results show Miffie achieves approximately 1.2 times higher accuracy than vanilla prompting across 1NF, 2NF, and 3NF, even for complex schemas like Advertising, Orders, and AirportDB.

Key takeaway

For data engineers managing relational databases, Miffie offers a significant reduction in manual normalization effort. You should consider implementing a dual-LLM self-refinement approach. Leverage models like GPT-4 for generation and o1-mini for verification to automate schema normalization up to 3NF. This method, combined with carefully crafted zero-shot prompts, delivers high accuracy and cost efficiency. It frees up time for more complex data architecture challenges.

Key insights

Miffie automates database normalization using a dual-LLM self-refinement loop with specialized zero-shot prompts for accuracy and cost-efficiency.

Principles

• Dual-model LLM architectures optimize domain-specific tasks.
• Iterative self-refinement enhances schema normalization accuracy.
• Task-specific zero-shot prompts improve LLM performance.

Method

Miffie's generation module (GPT-4) creates a schema, which the verification module (o1-mini) checks. If anomalies are found, feedback is provided for iterative refinement, repeating until normalization criteria are met or a maximum of three attempts.

In practice

• Use GPT-4 for schema generation.
• Employ o1-mini for schema verification.
• Limit refinement loops to three iterations.

Topics

• Database Normalization
• Large Language Models
• Self-Refinement Architecture
• Zero-Shot Prompting
• Data Integrity
• Schema Design

Best for: Research Scientist, Data Engineer, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.