Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration

Overview

The Chain-of-Query (CoQ) framework is designed to enhance SQL-aided table understanding through multi-agent collaboration. It aims to overcome limitations in existing methods, such as structural comprehension issues and error propagation.

Key Insights

• Framework Design: CoQ uses natural-language-style representations of table schemas to abstract structural noise.
• SQL Generation: It employs a clause-by-clause SQL generation strategy to improve query quality.
• Hybrid Reasoning: CoQ separates SQL-based mechanical reasoning from LLM-based logical inference.
• Performance: Experiments show CoQ improves accuracy and reduces invalid SQL rates.

BFSI Relevance

• Why Relevant: Accurate table understanding is crucial for data-driven decision-making in BFSI sectors.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Corporate Banking
• Actionable Implications: BFSI professionals should consider adopting multi-agent frameworks like CoQ to enhance data processing and decision-making capabilities.