Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios

Seq2sql: Generating structured queries from natural language using reinforcement learning

Original

Zhong, V.; Xiong, C.; and Socher, R. 2017 · 2017

Earlier work this paper cites.

Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task

Yu, T.; Zhang, R.; Yang, K.; Yasunaga, M.; Wang, D.; Li, Z.; Ma, J.; Li, I.; Yao, Q.; Roman, S.; et al. 2018 · 2018

Earlier work this paper cites.

RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers

Wang, B.; Shin, R.; Liu, X.; Polozov, O.; and Richardson, M. 2020 · 2020

Earlier work this paper cites.

Semantic Evaluation for Text-to-SQL with Distilled Test Suite

Zhong, R.; Yu, T.; and Klein, D. 2020 · 2020

Earlier work this paper cites.

Structure-Grounded Pretraining for Text-to-SQL

Deng, X.; Hassan, A.; Meek, C.; Polozov, O.; Sun, H.; and Richardson, M. 2021 · 2021

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Towards Robustness of Text-to-SQL Models against Synonym Substitution

Gan, Y.; Chen, X.; Huang, Q.; Purver, M.; Woodward, J. R.; Xie, J.; and Huang, P. 2021 · 2021

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Exploring Underexplored Limitations of Cross-Domain Text-to-SQL Generalization

Gan, Y.; Chen, X.; and Purver, M. 2021 · 2021

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SimCSE: Simple Contrastive Learning of Sentence Embeddings

Gao, T.; Yao, X.; and Chen, D. 2021 · 2021

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PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Scholak, T.; Schucher, N.; and Bahdanau, D. 2021 · 2021

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Training language models to follow instructions with human feedback

Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A.; et al. 2022 · 2022

Cited alongside, same era.

C3: Zero-shot Text-to-SQL with ChatGPT

Original

Dong, X.; Zhang, C.; Ge, Y.; Mao, Y.; Gao, Y.; Lin, J.; Lou, D.; et al. 2023 · 2023

Cited alongside, same era.

Text-to-sql empowered by large language models: A benchmark evaluation

Original

Gao, D.; Wang, H.; Li, Y.; Sun, X.; Qian, Y.; Ding, B.; and Zhou, J. 2023 · 2023

Cited alongside, same era.

StructGPT: A General Framework for Large Language Model to Reason over Structured Data

Jiang, J.; Zhou, K.; Dong, Z.; Ye, K.; Zhao, X.; and Wen, J.-R. 2023 · 2023

Cited alongside, same era.

API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs

Li, M.; Zhao, Y.; Yu, B.; Song, F.; Li, H.; Yu, H.; Li, Z.; Huang, F.; and Li, Y. 2023 · 2023

ACT-SQL: In-Context Learning for Text-to-SQL with Automatically-Generated Chain-of-Thought

Original

Zhang, H.; Cao, R.; Chen, L.; Xu, H.; and Yu, K. 2023 · 2023

Later among the works it cites.

SQLFixAgent: Towards Semantic-Accurate SQL Generation via Multi-Agent Collaboration

Original

Cen, J.; Liu, J.; Li, Z.; and Wang, J. 2024 · 2024

Closest in time.

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 · 2024

Closest in time.

Middleware for llms: Tools are instrumental for language agents in complex environments

Original

Gu, Y.; Shu, Y.; Yu, H.; Liu, X.; Dong, Y.; Tang, J.; Srinivasa, J.; Latapie, H.; and Su, Y. 2024 · 2024

Closest in time.

Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls