Results 1 to 10 of about 253,710 (347)

HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing [PDF]

arXiv, 2022
Recently, context-dependent text-to-SQL semantic parsing which translates natural language into SQL in an interaction process has attracted a lot of attention. Previous works leverage context-dependence information either from interaction history utterances or the previous predicted SQL queries but fail in taking advantage of both since of the mismatch
Yanzhao Zheng, Haibin Wang, B. Dong, Xingjun Wang, Changshan Li   +4 more
arxiv   +3 more sources

Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQL [PDF]

arXiv
Generating accurate SQL from users' natural language questions (text-to-SQL) remains a long-standing challenge due to the complexities involved in user question understanding, database schema comprehension, and SQL generation. Traditional text-to-SQL systems, which combine human engineering and deep neural networks, have made significant progress ...
Zijin Hong, Zheng Yuan, Qinggang Zhang, Hao Chen, Junnan Dong, Feiran Huang, Xiao Huang   +6 more
arxiv   +2 more sources

DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction [PDF]

Neural Information Processing Systems, 2023
We study the problem of decomposing a complex text-to-sql task into smaller sub-tasks and how such a decomposition can significantly improve the performance of Large Language Models (LLMs) in the reasoning process.
M. Pourreza, Davood Rafiei
semanticscholar   +1 more source

Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation [PDF]

Proceedings of the VLDB Endowment, 2023
Large language models (LLMs) have emerged as a new paradigm for Text-to-SQL task. However, the absence of a systematical benchmark inhibits the development of designing effective, efficient and economic LLM-based Text-to-SQL solutions.
Dawei Gao, Haibin Wang, Yaliang Li, Xiuyu Sun, Yichen Qian, Bolin Ding, Jingren Zhou   +6 more
semanticscholar   +1 more source

RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL [PDF]

AAAI Conference on Artificial Intelligence, 2023
One of the recent best attempts at Text-to-SQL is the pre-trained language model. Due to the structural property of the SQL queries, the seq2seq model takes the responsibility of parsing both the schema items (i.e., tables and columns) and the skeleton ...
Haoyang Li, Jing Zhang, Cuiping Li, Hong Chen   +3 more
semanticscholar   +1 more source

C3: Zero-shot Text-to-SQL with ChatGPT [PDF]

arXiv.org, 2023
This paper proposes a ChatGPT-based zero-shot Text-to-SQL method, dubbed C3, which achieves 82.3\% in terms of execution accuracy on the holdout test set of Spider and becomes the state-of-the-art zero-shot Text-to-SQL method on the Spider Challenge.
Xuemei Dong, C. Zhang, Yuhang Ge, Yuren Mao, Yunjun Gao, Lu Chen, Jinshu Lin, Dongfang Lou   +7 more
semanticscholar   +1 more source

A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability [PDF]

arXiv.org, 2023
This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation ...
Aiwei Liu, Xuming Hu, Lijie Wen, Philip S. Yu   +3 more
semanticscholar   +1 more source

Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing [PDF]

AAAI Conference on Artificial Intelligence, 2023
The task of text-to-SQL parsing, which aims at converting natural language questions into executable SQL queries, has garnered increasing attention in recent years. One of the major challenges in text-to-SQL parsing is domain generalization, i.e., how to
Jinyang Li, Binyuan Hui, Reynold Cheng, Bowen Qin, Chenhao Ma, Nan Huo, Fei Huang, Wenyu Du, Luo Si, Yongbin Li   +9 more
semanticscholar   +1 more source

From Prompt Injections to SQL Injection Attacks: How Protected is Your LLM-Integrated Web Application? [PDF]

arXiv.org, 2023
Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such
Rodrigo Pedro, Daniel Castro, Paulo Carreira, Nuno Santos   +3 more
semanticscholar   +1 more source

Enhancing Few-shot Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies [PDF]

arXiv.org, 2023
In-context learning (ICL) has emerged as a new approach to various natural language processing tasks, utilizing large language models (LLMs) to make predictions based on context that has been supplemented with a few examples or task-specific instructions.
Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir R. Radev   +7 more
semanticscholar   +1 more source