ProfiliTable: Multi-Agent Framework for Tabular Data Processing
ProfiliTable is a multi-agent framework that operates autonomously to streamline tasks related to table processing, including cleaning, transformation, augmentation, and matching. It overcomes the shortcomings of current LLM-based methods, which frequently generate code that is syntactically correct yet semantically incorrect due to vague instructions and insufficient structured feedback. The framework focuses on dynamic profiling, which involves building and continuously enhancing a cohesive execution context through interactive exploration, knowledge-enhanced synthesis, and feedback-driven adjustments. It comprises three main elements: a Profiler for ReAct-style data exploration, a Generator for sourcing curated operators to create task-aware code, and an Evaluator-Summarizer loop for incorporating execution feedback. The goal is to enhance reliability and accuracy within real-world data pipelines.
Key facts
- ProfiliTable is an autonomous multi-agent framework for table processing.
- It addresses issues in LLM-based automation of table tasks.
- The framework uses dynamic profiling to build semantic understanding.
- It includes a Profiler, Generator, and Evaluator-Summarizer components.
- The Profiler performs ReAct-style data exploration.
- The Generator retrieves curated operators for code synthesis.
- The Evaluator-Summarizer loop provides feedback-driven refinement.
- The system targets cleaning, transformation, augmentation, and matching tasks.
Entities
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