Text-to-SQL involves converting natural language questions into SQL queries to interact with databases is a complex task. Large Language Models (LLMs) have shown great promise in text-to-SQL.

There's no systematic way to evaluate LLMs for this task. This leads to issues like dataset overfitting and a lack of understanding about how to best use LLMs for specific sub-tasks involved in generating accurate SQL.

Requirement of a Comprehensive Benchmark

A comprehensive benchmark is needed to understand LLM capabilities for text-to-SQL and create better LLM-based solutions. This benchmark should go beyond the typical end-to-end accuracy measurement.

The Proposed Solution

The authors propose a detailed benchmark focusing on:

• Dataset Design: A dataset is needed that avoids overfitting by carefully considering question complexity, database size, and the types of knowledge required to answer questions.

• Five Core Tasks: The benchmark should evaluate models on these key sub-tasks involved in text-to-SQL:

  • Text-to-SQL (core task)

  • SQL Debugging (fixing errors)

  • SQL Optimization (making SQL more efficient)

  • Schema Linking (understanding database structure)

  • SQL-to-Text (explaining what an SQL query does)

• Prompt Engineering: Experiment with different prompt formats (the instructions given to the LLM) to find what works best.

• Model Variety Test different types and sizes of LLMs (general-purpose vs. code-specific) to see how they perform.

• Information Granularity: Test how the amount of context provided to the LLM impacts its accuracy with different learning strategies (zero-shot, few-shot).

Key Takeaways:

• Traditional machine learning methods for text-to-SQL have been outpaced by LLMs.

• A major focus of the proposed solution is avoiding overfitting models to specific datasets.

• Understanding the strengths and weaknesses of LLMs on the various sub-tasks will help design better text-to-SQL systems.

--> For complete details, refer to the paper.