Large language models (LLMs) know little about enterprise database tables in the private data ecosystem, which substantially differ from web text in structure and content. As LLMs' performance is tied to their training data, a crucial question is how useful they can be in improving enterprise database management and analysis tasks. To address this, we contribute experimental results on LLMs' performance for text-to-SQL and semantic column-type detection tasks on enterprise datasets. The performance of LLMs on enterprise data is significantly lower than on benchmark datasets commonly used. Informed by our findings and feedback from industry practitioners, we identify three fundamental challenges -- latency, cost, and quality -- and propose potential solutions to use LLMs in enterprise data workflows effectively.

翻译：大语言模型（LLMs）对私有数据生态中的企业数据库表知之甚少，这些数据在结构和内容上与网络文本存在显著差异。由于大语言模型的性能与其训练数据密切相关，一个关键问题是它们在改进企业数据库管理与分析任务方面能发挥多大效用。为此，我们针对企业数据集上的文本到SQL转换和语义列类型检测任务，贡献了大语言模型性能的实验结果。大语言模型在企业数据上的性能显著低于常用基准数据集上的表现。基于我们的发现及行业从业者的反馈，我们识别了三个根本性挑战——延迟、成本与质量，并提出了在企业数据工作流中有效运用大语言模型的潜在解决方案。