Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

Title:Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

Abstract:Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy database-level constraints. We study this issue through database normalization, involving reasoning about functional dependencies, lossless join decompositions, and inter-table constraints. We introduce a Database Normalization Benchmark (DNBENCH), comprising 3,275 samples for evaluating LLM-driven database normalization from 1NF to BCNF. DNBENCH uses a three-axis protocol to measure semantic equivalence, structural accuracy, and logical validity. Across Single, Complex, and Real World levels, DNBENCH uncovers recurring failures in dependency inference, schema decomposition, and inter-table constraint reconstruction. We further propose Multi-Agent Reasoning for Schemas (MARS), which separates evidence extraction, violation diagnosis, and decomposition planning from schema generation and verification. MARS improves the DNB-SCORE by 82.0% over the single-prompt baseline. All artifacts will be released upon acceptance.

Computation and Language (cs.CL)

arXiv:2609.11141 [cs.CL]

(or arXiv:2609.11141v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.11141 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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