Summary of Analyzing the Effectiveness Of Large Language Models on Text-to-sql Synthesis, by Richard Roberson et al.
Analyzing the Effectiveness of Large Language Models on Text-to-SQL Synthesis
by Richard Roberson, Gowtham Kaki, Ashutosh Trivedi
First submitted to arxiv on: 22 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Databases (cs.DB); Programming Languages (cs.PL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study explores various methods for using Large Language Models (LLMs) in Text-to-SQL program synthesis, focusing on outcomes and insights. The researchers employed the popular spider dataset, which involves inputting a natural language question along with a database schema to generate the correct SQL SELECT query. They fine-tuned local and open-source models as well as WizardLM’s WizardCoder-15B model, achieving high execution accuracy of 61% and 82.1%, respectively. The study also identifies seven categories of errors that can be categorized into what went wrong: incorrect column selection, grouping by the wrong column, predicting wrong values in conditionals, using different aggregates, extra or too few JOIN clauses, inconsistencies in the dataset, and incorrect query structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses large language models to help computers understand natural language questions and generate correct SQL code. The researchers tested different ways of using these models and found that they can be very accurate – up to 82% correct! They also looked at what happens when the models make mistakes, finding that most errors fall into seven categories. This study helps us understand how these large language models work and where they need improvement. |
