{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T06:00:18Z","timestamp":1759384818822,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>Text-to-SQL is a subtask in semantic parsing that has seen rapid progress with the evolution of Large Language Models (LLMs). However, LLMs face challenges due to hallucination issues and a lack of domain-specific database knowledge(such as table schema and cell values). As a result, they can make errors in generating table names, columns, and matching values to the correct columns in SQL statements. This paper introduces a method of knowledge injection to enhance LLMs\u2019 ability to understand schema contents by incorporating prior knowledge. This approach improves their performance in Text-to-SQL tasks. Experimental results show that pre-training LLMs on domain-specific database knowledge and fine-tuning them on downstream Text-to-SQL tasks significantly improves the Execution Match (EX) and Exact Match (EM) metrics across various models. This effectively reduces errors in generating column names and matching values to the columns. Furthermore, the knowledge-injected models can be applied to many downstream Text-to-SQL tasks, demonstrating the generalizability of the approach presented in this paper.<\/jats:p>","DOI":"10.3233\/faia240949","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:46:08Z","timestamp":1729172768000},"source":"Crossref","is-referenced-by-count":1,"title":["Enhancing Text-to-SQL Capabilities of Large Language Models via Domain Database Knowledge Injection"],"prefix":"10.3233","author":[{"given":"Xingyu","family":"Ma","sequence":"first","affiliation":[{"name":"School of Cyber Science & Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Tian","sequence":"additional","affiliation":[{"name":"Wuhan AI Research"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingxiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Foundation Model Research Center,Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuepeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Foundation Model Research Center,Institute of Automation, Chinese Academy of Sciences"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueming","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Cyber Science & Engineering, Huazhong University of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Foundation Model Research Center,Institute of Automation, Chinese Academy of Sciences"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences"},{"name":"Peng Cheng Laboratory"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240949","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:46:09Z","timestamp":1729172769000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240949"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240949","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}