In the rapidly growing landscape of human-database interactions, there is a need for advanced natural language processing and database management solutions that make these interactions easy and scalable. This whitepaper explores the management of these interactions as the needs evolve using the evaluation methodology of the text-to-SQL process, an integral part of the queries between users and database.
Artificial Intelligence
Multi-agent Collaborative Workflow for Text-to-SQL with Auto-evaluation
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Summary
When a non-technical user submits a query, the text-to-SQL system which is integrated to databases essentially converts this human language query into a SQL query to execute the search in the database.
However, the text-to-SQL systems come with certain limitations in accuracy, ambiguity handling, and managing complex queries. Hence, it is imperative to have a robust evaluation system that analyzes the performance, efficiency, and accuracy of the text-to-SQL system that is in use. While traditional human evaluation techniques exist, they are time-consuming, labor-intensive, and not scalable. An innovative and scalable solution lies in a multi-agent workflow-based auto-evaluation, which harnesses LLM (Leveraged Learning Models) to automatically evaluate the SQL response—a method that not only adapts seamlessly but also delivers superior performance.
However, the text-to-SQL systems come with certain limitations in accuracy, ambiguity handling, and managing complex queries. Hence, it is imperative to have a robust evaluation system that analyzes the performance, efficiency, and accuracy of the text-to-SQL system that is in use. While traditional human evaluation techniques exist, they are time-consuming, labor-intensive, and not scalable. An innovative and scalable solution lies in a multi-agent workflow-based auto-evaluation, which harnesses LLM (Leveraged Learning Models) to automatically evaluate the SQL response—a method that not only adapts seamlessly but also delivers superior performance.
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