On the Limitations of Large Language Models for Conceptual Database Modeling

AI Digest

论文分析LLMs在概念数据库建模中的局限性，实验显示其在复杂需求场景下易出现不一致和歧义，验证成本可能抵消效率提升。

This study reveals LLMs' limitations in conceptual database modeling, showing increased inconsistencies and ambiguities with complex requirements, where validation costs may offset productivity gains.

Key points

• 实验对比三种LLMs及不同提示技术在需求建模中的表现 Experiment compares three LLMs and prompting techniques in modeling requirements
• 复杂需求场景下模型出现结构/语义不一致和约束表达失败 Inconsistent structures/semantics and constraint failures emerge with complex requirements
• 当前LLMs尚未成熟，需人工验证确保建模可靠性 Current LLMs lack maturity for reliable modeling tasks
• 验证成本可能抵消LLMs带来的效率提升优势 Validation costs may offset productivity gains from LLMs

Takeaway: LLMs在复杂数据库建模中需人工验证，验证成本可能抵消效率提升 / LLMs require manual validation in complex modeling tasks, where validation costs may offset productivity gains

Why it matters 揭示LLMs在复杂场景下的局限性，为实际应用提供重要参考