Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis

Understand

This study investigates various approaches to using Large Language Models (LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insights derived.

• Employing the popular Text-to-SQL dataset, spider, the goal was to input a natural language question along with the database schema and output the correct SQL SELECT query.
• The initial approach was to fine-tune a local and open-source model to generate the SELECT query.
• After QLoRa fine-tuning WizardLM's WizardCoder-15B model on the spider dataset, the execution accuracy for generated queries rose to a high of 61%.