探索智能体数据系统中的语义鸿沟：分析工作流中操作化失败的形成性研究
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
AI总结：
本研究通过跨领域分析236个分析意图，识别出153种反复出现的操作化失败，归纳为五类语义鸿沟，揭示用户分析概念与系统可用信息之间的差距，并提出未来智能体数据系统需要更丰富的语义表示。
摘要：
大型语言模型（LLMs）越来越多地被用于生成查询、调用工具和构建分析工作流。尽管最近的进展显著改善了工作流的生成和执行，但操作化分析概念所需的语义信息往往超出了数据库模式和数据值中明确表示的范围。我们提出了一项跨领域的形成性研究，探讨智能体生成的分析工作流中的操作化失败。在涵盖金融、人力资源和公共安全领域的236个分析意图中，我们识别出153种反复出现的失败，尽管工作流生成和执行成功。我们的分析揭示了五类反复出现的失败：比较基础、过程推理、定量推理、角色混淆和政策基础。这些发现表明用户级分析概念与工作流生成系统可用信息之间存在语义鸿沟。更广泛地说，它们引发了对分析操作可接纳性的质疑，并表明未来的智能体数据系统可能需要更丰富的语义表示，以弥合分析意图与可执行计算之间的差距。
英文摘要：
Large language models (LLMs) are increasingly used to generate queries, invoke tools, and construct analytical workflows. Although recent advances have substantially improved workflow generation and execution, the semantic information required to operationalize analytical concepts often lies beyond what is explicitly represented in database schemas and data values. We present a cross-domain formative study of operationalization failures in agent-generated analytical workflows. Across 236 analytical intents spanning finance, human resources, and public safety domains, we identify 153 recurring failures despite successful workflow generation and execution. Our analysis reveals five recurring classes of failures: comparative grounding, process reasoning, quantitative reasoning, role confusion, and policy grounding. These findings suggest a semantic gap between user-level analytical concepts and the information available to workflow-generation systems. More broadly, they raise questions about the admissibility of analytical operations and suggest that future agentic data systems may require richer semantic representations to bridge the gap between analytical intent and executable computation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
