Summary of Booksql: a Large Scale Text-to-sql Dataset For Accounting Domain, by Rahul Kumar and Amar Raja Dibbu and Shrutendra Harsola and Vignesh Subrahmaniam and Ashutosh Modi
BookSQL: A Large Scale Text-to-SQL Dataset for Accounting Domain
by Rahul Kumar, Amar Raja Dibbu, Shrutendra Harsola, Vignesh Subrahmaniam, Ashutosh Modi
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper aims to address the lack of large-scale datasets for natural language interfaces to databases in essential domains like finance and accounting. The authors introduce a new dataset, BookSQL, comprising 100k natural language queries-SQL pairs and accounting databases with 1 million records. They experiment with state-of-the-art models, including GPT-4, on the BookSQL dataset, revealing significant performance gaps that suggest developing more focused models for this domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary BookSQL is a new large-scale dataset designed to help people without technical backgrounds interact with accounting and financial databases using natural language queries. The dataset contains 100k pairs of questions and answers, along with an accounting database containing 1 million records. Researchers are trying different methods to see how well they work on this new dataset. |
Keywords
» Artificial intelligence » Gpt
