On the Limitations of Large Language Models for Conceptual Database Modeling

TL;DR

This paper evaluates the capabilities and limitations of Large Language Models in automatically generating Entity-Relationship diagrams from natural language requirements, highlighting their decreasing reliability with increasing complexity.

Contribution

It provides an empirical analysis of LLMs' effectiveness in conceptual database modeling and identifies current limitations in handling complex scenarios.

Findings

LLMs perform reasonably in simple scenarios

Reliability decreases as complexity increases

Inconsistencies and ambiguities rise with complexity

Abstract

This article analyzes the use of Large Language Models (LLMs) as support for the conceptual modeling of relational databases through the automatic generation of Entity-Relationship (ER) diagrams from natural language requirements. The approach combines different language models with prompt engineering techniques to evaluate their ability to identify entities, relationships, and attributes in a conceptually consistent manner. The experimental evaluation involved three LLMs, each subjected to three prompting techniques (Zero-Shot, Chain of Thought, and Chain of Thought + Verifier), applied to the same requirements scenario with progressively increasing complexity. The generated diagrams were qualitatively analyzed through direct comparison with the textual requirements, considering the structural and semantic adherence of the modeled elements. The results indicate that, although LLMs show reasonable performance in less complex scenarios, their reliability decreases as the complexity of the requirements increases, with a rise in inconsistencies, ambiguities, and failures in representing constraints. These findings reinforce that, in their current state, LLMs are not sufficiently mature for reliable use in complex scenarios, and the cost of validation may offset the apparent productivity gains.