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Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL.
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Attention is all you need
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Seq2sql: Generating structured queries from natural language using reinforcement learning
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task
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Relational graph attention networks
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Towards complex text-to-sql in cross-domain database with intermediate representation
Guo, J., Zhan, Z., Gao, Y., Xiao, Y., Lou, J.-G., Liu, T., and Zhang, D · 2019
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Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Wang, B., Shin, R., Liu, X., Polozov, O., and Richardson, M · 2019
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task, 2019
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., Zhang, Z., and Radev, D · 2019
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Language models are few-shot learners
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Tabert: Pretraining for joint understanding of textual and tabular data
Yin, P., Neubig, G., Yih, W.-t., and Riedel, S · 2020
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Lgesql: line graph enhanced text-to-sql model with mixed local and non-local relations
Cao, R., Chen, L., Chen, Z., Zhao, Y., Zhu, S., and Yu, K · 2021
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Evaluating large language models trained on code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al · 2021
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Ryansql: Recursively applying sketch-based slot fillings for complex text-to-sql in cross-domain databases
Choi, D., Shin, M. C., Kim, E., and Shin, D. R · 2021
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What makes good in-context examples for gpt- 3 3 ?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., and Chen, W · 2021
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Understanding the properties of minimum Bayes risk decoding in neural machine translation
Müller, M. and Sennrich, R · 2021
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Picard: Parsing incrementally for constrained auto-regressive decoding from language models
Scholak, T., Schucher, N., and Bahdanau, D · 2021
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Grappa: Grammar-augmented pre-training for table semantic parsing
Yu, T., Wu, C.-S., Lin, X. V., bailin wang, Tan, Y. C., Yang, X., Radev, D., richard socher, and Xiong, C · 2021
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Towards knowledge-intensive text-to-SQL semantic parsing with formulaic knowledge
Dou, L., Gao, Y., Liu, X., Pan, M., Wang, D., Che, W., Zhan, D., Kan, M.-Y., and Lou, J.-G · 2022
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S 2 sql: Injecting syntax to question-schema interaction graph encoder for text-to-sql parsers, 2022
Hui, B., Geng, R., Wang, L., Qin, B., Li, B., Sun, J., and Li, Y · 2022
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Rasat: Integrating relational structures into pretrained seq2seq model for text-to-sql
Qi, J., Tang, J., He, Z., Wan, X., Cheng, Y., Zhou, C., Wang, X., Zhang, Q., and Lin, Z · 2022
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Evaluating the text-to-sql capabilities of large language models
Rajkumar, N., Li, R., and Bahdanau, D · 2022
Decoding by contrasting knowledge: Enhancing llms’ confidence on edited facts
Bi, B., Liu, S., Mei, L., Wang, Y., Ji, P., and Cheng, X · 2024
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E-sql: Direct schema linking via question enrichment in text-to-sql
Caferoğlu, H. A. and Ulusoy, Ö · 2024
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Cost-efficient knowledge-based question answering with large language models
Dong, J., Zhang, Q., Zhou, C., Chen, H., Zha, D., and Huang, X · 2024
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Text-to-sql empowered by large language models: A benchmark evaluation
Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., and Zhou, J · 2024
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Spider 2.0: Evaluating language models on real-world enterprise text-to-sql workflows
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Natural language to code translation with execution
Shi, F., Fried, D., Ghazvininejad, M., Zettlemoyer, L., and Wang, S. I · 2022
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Codexdb: Synthesizing code for query processing from natural language instructions using gpt-3 codex
Trummer, I · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al · 2023
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How to prompt LLMs for text-to-SQL: A study in zero-shot, single-domain, and cross-domain settings
Chang, S. and Fosler-Lussier, E · 2023
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C3: Zero-shot text-to-sql with chatgpt, 2023
Dong, X., Zhang, C., Ge, Y., Mao, Y., Gao, Y., lu Chen, Lin, J., and Lou, D · 2023
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Lei, F., Chen, J., Ye, Y., Cao, R., Shin, D., Su, H., Suo, Z., Gao, H., Hu, W., Yin, P., et al · 2024
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Epi-sql: Enhancing text-to-sql translation with error-prevention instructions
Liu, X. and Tan, Z · 2024
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Chase-sql: Multi-path reasoning and preference optimized candidate selection in text-to-sql
Pourreza, M., Li, H., Sun, R., Chung, Y., Talaei, S., Kakkar, G. T., Gan, Y., Saberi, A., Ozcan, F., and Arik, S. O · 2024
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Purple: Making a large language model a better sql writer
Ren, T., Fan, Y., He, Z., Huang, R., Dai, J., Huang, C., Jing, Y., Zhang, K., Yang, Y., and Wang, X. S · 2024
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Chess: Contextual harnessing for efficient sql synthesis
Talaei, S., Pourreza, M., Chang, Y.-C., Mirhoseini, A., and Saberi, A · 2024
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Mac-sql: A multi-agent collaborative framework for text-to-sql
Wang, B., Ren, C., Yang, J., Liang, X., Bai, J., Chai, L., Yan, Z., Zhang, Q.-W., Yin, D., Sun, X., et al · 2024
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Symbol-llm: Towards foundational symbol-centric interface for large language models
Xu, F., Wu, Z., Sun, Q., Ren, S., Yuan, F., Yuan, S., Lin, Q., Qiao, Y., and Liu, J · 2024
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Evaluating world models with llm for decision making
Yang, C., Wang, X., Jiang, J., Zhang, Q., and Huang, X · 2024
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Knowgpt: Knowledge graph based prompting for large language models
Zhang, Q., Dong, J., Chen, H., Zha, D., Yu, Z., and Huang, X · 2024
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Enhancing explainable rating prediction through annotated macro concepts
Zhou, H., Zhou, S., Chen, H., Liu, N., Yang, F., and Huang, X · 2024
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Structlm: Towards building generalist models for structured knowledge grounding
Zhuang, A., Zhang, G., Zheng, T., Du, X., Wang, J., Ren, W., Huang, S. W., Fu, J., Yue, X., and Chen, W · 2024
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Parameters vs. context: Fine-grained control of knowledge reliance in language models
Bi, B., Liu, S., Wang, Y., Xu, Y., Fang, J., Mei, L., and Cheng, X · 2025
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Safemlrm: Demystifying safety in multi-modal large reasoning models
Fang, J., Wang, Y., Wang, R., Yao, Z., Wang, K., Zhang, A., Wang, X., and Chua, T.-S · 2025
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Safety in large reasoning models: A survey
Wang, C., Liu, Y., Li, B., Zhang, D., Li, Z., and Fang, J · 2025
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Knapsack optimization-based schema linking for llm-based text-to-sql generation
Yuan, Z., Chen, H., Hong, Z., Zhang, Q., Huang, F., and Huang, X · 2025
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A survey of graph retrieval-augmented generation for customized large language models
Zhang, Q., Chen, S., Bei, Y., Yuan, Z., Zhou, H., Hong, Z., Dong, J., Chen, H., Chang, Y., and Huang, X · 2025
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Text-attributed graph learning with coupled augmentations
Zhou, C., Du, J., Zhou, H., Chen, H., Huang, F., and Huang, X · 2025
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