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AI Research
arXiv CS.AI·Jul 2·5 min read
Exploring the Semantic Gap in Agentic Data Systems: A Formative Study of Operationalization Failures in Analytical Workflows
TL;DR
A significant issue exists in agentic data systems, where operationalization failures hinder effective analytical workflows. This study utilized qualitative methods to identify and analyze these failures, focusing on the semantic gap—discrepancies between intended and actual meanings in data interpretation.
✦ Why It Matters
Engineers can enhance data workflows by standardizing terminology and improving communication around data definitions.
Key Takeaways
- Assess the semantic richness of your data schemas.
- Identify common operationalization failures in your workflows.
- Consider integrating richer semantic representations in your systems.
- Explore cross-domain applications of LLMs for better insights.
Full Summary
Large language models (LLMs) are increasingly utilized to automate the generation of analytical workflows, yet they often struggle with operationalizing complex analytical concepts. A formative study examined 236 analytical intents in finance, human resources, and public safety, uncovering 153 recurring failures despite successful workflow execution.
The analysis categorized these failures into five classes: comparative grounding, process reasoning, quantitative reasoning, role confusion, and policy grounding. These issues indicate a significant semantic gap between the analytical concepts users intend to express and the data representations available to LLMs.
The findings suggest that future agentic data systems need to incorporate richer semantic representations to effectively bridge this gap, enhancing the accuracy and relevance of generated workflows.