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In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks.
Language models are few-shot learners
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Systematic mistake analysis of digital computer programs
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Expanding the scope of the atis task: The atis-3 corpus
Deborah A Dahl, Madeleine Bates, Michael K Brown, William M Fisher, Kate Hunicke-Smith, David S Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriberg. 1994 · 1994
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Learning to parse database queries using inductive logic programming
John M Zelle and Raymond J Mooney. 1996 · 1996
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Learning a neural semantic parser from user feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer. 2017 · 2017
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Sqlizer: query synthesis from natural language
Navid Yaghmazadeh, Yuepeng Wang, Isil Dillig, and Thomas Dillig. 2017 · 2017
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Neural compositional denotational semantics for question answering
Nitish Gupta and Mike Lewis. 2018 · 2018
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al. 2018 · 2018
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2019 · 2019
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Model-based interactive semantic parsing: A unified framework and a text-to-SQL case study
Ziyu Yao, Yu Su, Huan Sun, and Wen-tau Yih. 2019 · 2019
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“what do you mean by that?” a parser-independent interactive approach for enhancing text-to-SQL
Yuntao Li, Bei Chen, Qian Liu, Yan Gao, Jian-Guang Lou, Yan Zhang, and Dongmei Zhang. 2020 · 2020
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Rat-sql: Relation-aware schema encoding and linking for text-to-sql parsers
Bailin Wang, Richard Shin, Xiaodong Liu, Oleksandr Polozov, and Matthew Richardson. 2020 · 2020
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Break it down: A question understanding benchmark
Tomer Wolfson, Mor Geva, Ankit Gupta, Matt Gardner, Yoav Goldberg, Daniel Deutch, and Jonathan Berant. 2020 · 2020
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An imitation game for learning semantic parsers from user interaction
Ziyu Yao, Yiqi Tang, Wen-tau Yih, Huan Sun, and Yu Su. 2020 · 2020
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Photon: A robust cross-domain text-to-SQL system
Jichuan Zeng, Xi Victoria Lin, Steven C.H. Hoi, Richard Socher, Caiming Xiong, Michael Lyu, and Irwin King. 2020 · 2020
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Semantic evaluation for text-to-sql with distilled test suites
Ruiqi Zhong, Tao Yu, and Dan Klein. 2020 · 2020
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. 2021 · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
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Towards transparent interactive semantic parsing via step-by-step correction
Lingbo Mo, Ashley Lewis, Huan Sun, and Michael White. 2022 · 2022
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RASAT: Integrating relational structures into pretrained Seq2Seq model for text-to-SQL
Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, and Zhouhan Lin. 2022 · 2022
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Evaluating the text-to-sql capabilities of large language models
Nitarshan Rajkumar, Raymond Li, and Dzmitry Bahdanau. 2022 · 2022
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Weakly supervised text-to-sql parsing through question decomposition
Tomer Wolfson, Daniel Deutch, and Jonathan Berant. 2022 · 2022
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Structure-grounded pretraining for text-to-sql
Xiang Deng, Ahmed Hassan, Christopher Meek, Oleksandr Polozov, Huan Sun, and Matthew Richardson. 2021 · 2021
Cited alongside, same era.
NL-EDIT: Correcting semantic parse errors through natural language interaction
Ahmed Elgohary, Christopher Meek, Matthew Richardson, Adam Fourney, Gonzalo Ramos, and Ahmed Hassan Awadallah. 2021 · 2021
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Diy: Assessing the correctness of natural language to sql systems
Arpit Narechania, Adam Fourney, Bongshin Lee, and Gonzalo Ramos. 2021 · 2021
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Picard: Parsing incrementally for constrained auto-regressive decoding from language models
Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau. 2021 · 2021
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Compositional generalization and natural language variation: Can a semantic parsing approach handle both?
Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. 2021 · 2021
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Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Caiming Xiong, et al. 2021 · 2021
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. 2023 · 2023
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Selective annotation makes language models better few-shot learners
SU Hongjin, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A Smith, et al. 2023 · 2023
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Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-Tau Yih, Sida Wang, and Xi Victoria Lin. 2023 · 2023
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Din-sql: Decomposed in-context learning of text-to-sql with self-correction
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
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Speak to your parser: Interactive text-to-SQL with natural language feedback
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