Large Language Models (LLMs) promise to automate data engineering on tabular data, offering enterprises a valuable opportunity to cut the high costs of manual data handling. But the enterprise domain comes with unique challenges that existing LLM-based approaches for data engineering often overlook, such as large table sizes, more complex tasks, and the need for internal knowledge. To bridge these gaps, we identify key enterprise-specific challenges related to data, tasks, and background knowledge and extensively evaluate how they affect data engineering with LLMs. Our analysis reveals that LLMs face substantial limitations in real-world enterprise scenarios, with accuracy declining sharply. Our findings contribute to a systematic understanding of LLMs for enterprise data engineering to support their adoption in industry.

翻译：大语言模型（LLMs）有望实现对表格数据的数据工程自动化，为企业提供了降低高昂人工数据处理成本的重要机遇。然而，企业领域存在独特的挑战，现有基于LLM的数据工程方法往往忽视了这些挑战，例如大规模表格、更复杂的任务以及对内部知识的需求。为弥补这些差距，我们识别了与数据、任务和背景知识相关的关键企业特定挑战，并广泛评估了它们如何影响基于LLM的数据工程。我们的分析表明，在真实企业场景中，LLMs面临显著局限性，准确性急剧下降。我们的研究结果有助于系统理解LLM在企业数据工程中的应用，以支持其在工业界的采纳。