Summary of Datagpt-sql-7b: An Open-source Language Model For Text-to-sql, by Lixia Wu et al.
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL
by Lixia Wu, Peng Li, Junhong Lou, Lei Fu
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed suite of compact, fine-tuned models and self-refine mechanisms aim to democratize data access and analysis for non-expert users by translating natural language queries into SQL commands. The system, DataGpt-sql, was constructed with a dataset of over 20K sample Text-to-SQL queries and achieved an accuracy of 87.2% on the spider-dev benchmark. To ensure code validity, a code corrector was integrated into the model. The solution showcases the effectiveness of text-to-SQL conversion tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers created a new way for people to ask questions in natural language and get answers in SQL format. They made a special dataset with many examples of this kind of question-and-answer pair, which helped improve their system’s ability to generate correct SQL code. To make sure the generated code is accurate, they added a “corrector” feature that checks the code for mistakes. The new system, called DataGpt-sql, worked well and could accurately translate natural language queries into SQL commands. |
