用双模型自修正自动完成数据库规范化，省时又准确。
Database Normalization via Dual-LLM Self-Refinement
- 采用生成与验证双模型架构，自动迭代优化表结构
- 在复杂数据库上保持高准确率，无需人工干预
- 适合数据工程师和自动化系统开发人员使用
数据库规范化对保障数据完整性至关重要，但通常由数据工程师手动完成，耗时且易出错。为此，我们提出 Miffie 框架，利用大语言模型实现无需人工参与的自动化数据库规范化，并保持高准确性。Miffie 的核心是双模型自修正架构，分别使用表现最优的模型进行规范化模式生成与验证。生成模块根据验证模块的反馈消除异常，直至输出符合规范要求的模式。我们还设计了任务特定的零样本提示，以兼顾准确率与成本效率。实验结果表明，Miffie 能够对复杂数据库模式进行规范化，同时维持高准确性。
原文摘要 · Abstract (English)
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.
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