DataLab unifies Business Intelligence workflows by integrating LLM-powered agents with computational notebooks, enabling collaboration between data engineers, scientists, and analysts in a single environment.
https://arxiv.org/abs/2412.02205
🤔 Original Problem:
→ Current Business Intelligence tools are fragmented across different roles and tasks, causing inefficiencies and errors.
→ Existing LLM solutions focus on individual tasks without considering the entire BI workflow.
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🛠️ Solution in this Paper:
→ DataLab introduces a unified platform combining LLM agents with computational notebooks.
→ A domain knowledge incorporation module enhances enterprise-specific task performance.
→ An inter-agent communication mechanism enables structured information sharing.
→ Cell-based context management improves efficiency in computational notebooks.
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💡 Key Insights:
→ Domain knowledge can be automatically extracted from existing data processing scripts
→ Structured communication between agents performs better than natural language
→ Dynamic context management is crucial for maintaining system efficiency
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📊 Results:
→ 58.58% increase in accuracy on enterprise-specific tasks
→ 61.65% reduction in token cost
→ State-of-the-art performance on research benchmarks
→ Successfully deployed at Tencent with real-world datasets
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