Search Results - Text2SQL

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  1. 1

    LI-EMRSQL: Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records by Li, Qing, You, Tao, Chen, Jinchao, Zhang, Ying, Du, Chenglie

    ISSN: 0018-9529, 1558-1721
    Published: New York IEEE 01.06.2024
    Published in IEEE transactions on reliability (01.06.2024)
    “… terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new database…”
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    Journal Article
  2. 2

    Text2SQL Business Intelligence System Based on Retrieval‐Augmented Generation (RAG) by Liu, Jie, Chu, Shiwei

    ISSN: 2577-8196, 2577-8196
    Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2025
    Published in Engineering reports (Hoboken, N.J.) (01.06.2025)
    “… Advances in natural language processing, particularly deep learning generative models, have enabled text‐to‐SQL (text2SQL…”
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    Journal Article
  3. 3

    LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks Under Low-Resource Scenarios by Wen, Wuzhenghong, Zhang, Yongpan, Pan, Su, Sun, Yuwei, Lu, Pengwei, Ding, Cheng

    ISSN: 2079-9292, 2079-9292
    Published: Basel MDPI AG 01.09.2025
    Published in Electronics (Basel) (01.09.2025)
    “…In supervised fine-tuning (SFT) for Text2SQL tasks, particularly for databases with numerous tables, encoding schema features requires excessive tokens, escalating GPU resource requirements during fine-tuning…”
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    Journal Article
  4. 4

    M-SQL: Multi-task Representation Learning for Single-Table Text2SQL Generation by Zhang, Xiaoyu, Yin, Fengjing, Ma, Guojie, Ge, Bin, Xiao, Weidong

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 01.01.2020
    Published in IEEE access (01.01.2020)
    “…Text2SQL can help non-professionals connect with databases by turning natural languages into SQL…”
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    Journal Article
  5. 5
  6. 6

    F-SQL: Fuse Table Schema and Table Content for Single-Table Text2SQL Generation by Zhang, Xiaoyu, Yin, Fengjing, Ma, Guojie, Ge, Bin, Xiao, Weidong

    ISSN: 2169-3536, 2169-3536
    Published: Piscataway IEEE 2020
    Published in IEEE access (2020)
    “…Automatically parsing SQL queries from natural languages can help non-professionals access databases and improve the efficiency of information utilization. It…”
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    Journal Article
  7. 7

    Chat with MES: LLM-driven user interface for manipulating garment manufacturing system through natural language by Yuan, Zhaolin, Li, Ming, Liu, Chang, Han, Fangyuan, Huang, Haolun, Dai, Hong-Ning

    ISSN: 0278-6125
    Published: Elsevier Ltd 01.06.2025
    Published in Journal of manufacturing systems (01.06.2025)
    “…This paper presents Chat with MES (CWM), an AI agent system, which integrates LLMs into the Manufacturing Execution System (MES), serving as the “ears, mouth,…”
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    Journal Article
  8. 8

    Evaluating Fine-Tuned LLMs with RAG and Text2SQL for the Water Conservancy Domain by Huang, Zheyuan, Ma, Shuaisen, Wang, Hongxia, Zheng, Zilong, Yu, Jiahui, Xia, Yifan

    ISSN: 2159-1288
    Published: IEEE 25.04.2025
    “…) and Retrieval-Augmented Generation (RAG). By integrating RAG for accurate information retrieval from a dynamic knowledge hub and employing fine-tuned Text2SQL for querying real-time water level databases, the system enhances decision support…”
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    Conference Proceeding
  9. 9

    Dairy GPT: Empowering dairy farmers to interact with numerical databases through natural language conversations by Gontijo, Danillo, Rolins Santana, Douglas, de Assis Costa, Gustavo, Cabrera, Victor E., Noronha de Andrade Freitas, Eduardo

    ISSN: 2772-3755, 2772-3755
    Published: Elsevier B.V 01.12.2025
    Published in Smart agricultural technology (01.12.2025)
    “…Large language models (LLMs), like GPT-4, have revolutionized artificial intelligence by enabling intuitive text and voice interactions, simplifying complex…”
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    Journal Article
  10. 10

    Research on BERT-based Text2SQL Multi-task Learning by Xusheng, Liu, Yeteng, An, Jingxian, Lv, Huimin, Zhang, Yumeng, Zhang, Min, Li, Wei, Zhao, Wei, Han, Liangfei, Sun, Huiqin, Li

    Published: IEEE 29.01.2023
    “…With the continuous development of computer technology, the Internet has entered the era of big data, which has spawned massive data in various fields and…”
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    Conference Proceeding
  11. 11

    Optimizing Customer Support Using Text2SQL to Query Natural Language Databases by Maj, Michał, Pliszczuk, Damian, Marek, Patryk, Wilczewska, Weronika, Przysucha, Bartosz, Rymarczyk, Tomasz

    ISSN: 1108-2976
    Published: Anixis Professor El Thalassinos 01.01.2024
    Published in European Research Studies (01.01.2024)
    “…Purpose: This paper explores the challenges and potential solutions associated with integrating Text2SQL technology into customer support operations…”
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    Journal Article
  12. 12

    A survey on complex factual question answering by Zhang, Lingxi, Zhang, Jing, Ke, Xirui, Li, Haoyang, Huang, Xinmei, Shao, Zhonghui, Cao, Shulin, Lv, Xin

    ISSN: 2666-6510, 2666-6510
    Published: Elsevier B.V 2023
    Published in AI open (2023)
    “…Answering complex factual questions has drawn a lot of attention. Researchers leverage various data sources to support complex QA, such as unstructured texts,…”
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    Journal Article
  13. 13

    CRUSH4SQL: Collective Retrieval Using Schema Hallucination For Text2SQL by Kothyari, Mayank, Dhingra, Dhruva, Sarawagi, Sunita, Chakrabarti, Soumen

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 02.11.2023
    Published in arXiv.org (02.11.2023)
    “…Existing Text-to-SQL generators require the entire schema to be encoded with the user text. This is expensive or impractical for large databases with tens of…”
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    Paper
  14. 14

    LR-SQL: A Supervised Fine-Tuning Method for Text2SQL Tasks under Low-Resource Scenarios by Wen Wuzhenghong, Zhang Yongpan, Pan, Su, Sun, Yuwei, Lu Pengwei, Cheng, Ding

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 15.10.2024
    Published in arXiv.org (15.10.2024)
    “…Large language models revolutionize Text2SQL through supervised fine-tuning, yet a crucial limitation is overlooked…”
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    Paper
  15. 15

    Text2SQL is Not Enough: Unifying AI and Databases with TAG by Biswal, Asim, Patel, Liana, Jha, Siddarth, Kamsetty, Amog, Liu, Shu, Gonzalez, Joseph E, Guestrin, Carlos, Zaharia, Matei

    ISSN: 2331-8422
    Published: Ithaca Cornell University Library, arXiv.org 27.08.2024
    Published in arXiv.org (27.08.2024)
    “… However, existing methods and benchmarks insufficiently explore this setting. Text2SQL methods focus solely on natural language questions that can be…”
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    Paper
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    Evaluating ChatGPT for Text-To-SQL: An Empirical Approach by Petkovic, Dusan

    Published: IEEE 07.08.2025
    “…Since the existence of database systems, the querying of data managed by them has been one of the most important and challenging task for their users. For this…”
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    Conference Proceeding
  19. 19

    SEED: Enhancing Text-to-SQL Performance and Practical Usability Through Automatic Evidence Generation by Yun, Janghyeon, Lee, Sang-goo

    ISSN: 2473-3490
    Published: IEEE 19.05.2025
    “…Text-to-SQL enables non-experts to retrieve data from databases by converting natural language queries into SQL. However, state-of-the-art text-to-SQL studies…”
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    Conference Proceeding
  20. 20

    T2I-RISE: Text-to-Insights with Reinforcement learning, Integration of Semantic layers and Enrichment - A Comprehensive Approach with Conversational Context and Feedback Systems by Katikeri, Raghu, Phaniraja, Sai, Pratheek, Sai, Desai, Rajvi, Vaid, Amit, Shukla, Neelesh, Jain, Sandeep

    ISSN: 2573-2978
    Published: IEEE 15.12.2024
    Published in IEEE International Conference on Big Data (15.12.2024)
    “… Despite the industry's increasing use of Text2SQL powered by Large Language Models (LLM), implementation challenges remain, particularly in managing large-scale database schema representations…”
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    Conference Proceeding