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arXiv AI By Arthur F. Siqueira, Carlos D. S. Nogueira, Eduarda Farias, Claudio E. C. Campelo, J\'ulia Menezes

On the Limitations of Large Language Models for Conceptual Database Modeling

Read the original on arXiv AI →

The article examines how Large Language Models can aid in creating Entity-Relationship diagrams from natural language requirements. It tests three LLMs with three prompting strategies—Zero-Shot, Chain of Thought, and Chain of Thought + Verifier—on scenarios of increasing complexity. Findings show that while LLMs perform adequately on simpler tasks, their reliability drops with more complex requirements, leading to inconsistencies, ambiguities, and constraint representation failures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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