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This study investigates various approaches to using Large Language Models(LLMs) for Text-to-SQL program synthesis, focusing on the outcomes and insightsderived. Employing the popular Text-to-SQL dataset, spider, the goal was toinput a natural language question along with the database schema and output thecorrect SQL SELECT query. The initial approach was to fine-tune a local andopen-source model to generate the SELECT query. After QLoRa fine-tuningWizardLM's WizardCoder-15B model on the spider dataset, the execution accuracyfor generated queries rose to a high of 61%. With the second approach, usingthe fine-tuned gpt-3.5-turbo-16k (Few-shot) + gpt-4-turbo (Zero-shot errorcorrection), the execution accuracy reached a high of 82.1%. Of all theincorrect queries, most can be categorized into a seven different categories ofwhat went wrong: selecting the wrong columns or wrong order of columns,grouping by the wrong column, predicting the wrong values in conditionals,using different aggregates than the ground truth, extra or too few JOINclauses, inconsistencies in the Spider dataset, and lastly completely incorrectquery structure. Most if not all of the queries fall into these categories andit is insightful to understanding where the faults still lie with LLM programsynthesis and where they can be improved.

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