{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/datagpt-sql-7b-an-open-source-language-model-1","title":"DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL","arxiv_id":"2409.15985","date":"2024-09-24","proceeding":null,"authors":["Lixia Wu","Peng Li","Junhong Lou","Lei Fu"],"abstract":"In addressing the pivotal role of translating natural language queries into SQL commands, we propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users, mitigating risks associated with closed-source Large Language Models. Specifically, we constructed a dataset of over 20K sample for Text-to-SQL as well as the preference dateset, to improve the efficiency in the domain of SQL generation. To further ensure code validity, a code corrector was integrated into the model. Our system, DataGpt-sql, achieved 87.2\\% accuracy on the spider-dev, respectively, showcasing the effectiveness of our solution in text-to-SQL conversion tasks. Our code, data, and models are available at \\url{https://github.com/CainiaoTechAi/datagpt-sql-7b}","url_abs":"https://arxiv.org/abs/2409.15985v1","url_pdf":"https://arxiv.org/pdf/2409.15985v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"natural-language-queries","task_name":"Natural Language Queries"},{"task_slug":"text-to-sql","task_name":"Text to SQL"},{"task_slug":"text-to-sql","task_name":"Text-To-SQL"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-to-sql-on-spider","task":"Text-To-SQL","dataset":"spider","model":"datagpt-sql-7B + InvalidSQL-Feedback","rank_in_archive_order":15,"of":20,"metrics":{"Exact Match Accuracy (Dev)":"81.6","Execution Accuracy (Dev)":"87.2"},"uses_additional_data":true},{"leaderboard":"/sota/text-to-sql-on-spider","task":"Text-To-SQL","dataset":"spider","model":"datagpt-sql-7B","rank_in_archive_order":16,"of":20,"metrics":{"Exact Match Accuracy (Dev)":"80.3","Execution Accuracy (Dev)":"84.8"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}