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Large language models (LLMs) have achieved impressive performance on code generation.
The pragmatic programmer: from journeyman to master, 2000
Andrew Hunt and David Thomas · 2000
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Language to logical form with neural attention
Li Dong and Mirella Lapata · 2016
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Robustfill: Neural program learning under noisy i/o
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Deepfix: Fixing common c language errors by deep learning
Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade · 2017
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Learning a neural semantic parser from user feedback
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Jayant Krishnamurthy, and Luke Zettlemoyer · 2017
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Leveraging grammar and reinforcement learning for neural program synthesis
Rudy Bunel, Matthew Hausknecht, Jacob Devlin, Rishabh Singh, and Pushmeet Kohli · 2018
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Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song · 2018
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Mapping language to code in programmatic context
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer · 2018
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Dynamic neural program embedding for program repair
Ke Wang, Rishabh Singh, and Zhendong Su · 2018
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song · 2019
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Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, and Jishen Zhao · 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
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Reranking for neural semantic parsing
Pengcheng Yin and Graham Neubig · 2019
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CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases
Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, Yi Chern Tan, Tianze Shi, Zihan Li, Youxuan Jiang, Michihiro Yasunaga, Sungrok Shim, Tao Chen, Alexander Fabbri, Zifan Li, Luyao Chen, Yuwen Zhang, Shreya Dixit, Vincent Zhang, Caiming Xiong, Richard Socher, Walter Lasecki, and Dragomir Radev · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Speak to your parser: Interactive text-to-SQL with natural language feedback
Ahmed Elgohary, Saghar Hosseini, and Ahmed Hassan Awadallah · 2020
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Synthesize, execute and debug: Learning to repair for neural program synthesis
Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, and Dawn Song · 2020
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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RAT-SQL: Relation-aware schema encoding and linking for text-to-SQL parsers
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Graph-based, self-supervised program repair from diagnostic feedback
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Program synthesis with large language models
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Measuring coding challenge competence with APPS
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt · 2021
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Show your work: Scratchpads for intermediate computation with language models
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PICARD: Parsing incrementally for constrained auto-regressive decoding from language models
Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, and Jason Wei · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
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N-best hypotheses reranking for text-to-sql systems
Lu Zeng, Sree Hari Krishnan Parthasarathi, and Dilek Hakkani-Tur · 2022
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Torsten Scholak, Nathan Schucher, and Dzmitry Bahdanau · 2021
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Michihiro Yasunaga and Percy Liang · 2021
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Constitutional ai: Harmlessness from ai feedback
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A data-driven approach for learning to control computers
Peter C Humphreys, David Raposo, Tobias Pohlen, Gregory Thornton, Rachita Chhaparia, Alistair Muldal, Josh Abramson, Petko Georgiev, Adam Santoro, and Timothy Lillicrap · 2022
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Decomposed prompting: A modular approach for solving complex tasks
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Multi-lingual evaluation of code generation models
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Language models can solve computer tasks
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Codegen: An open large language model for code with multi-turn program synthesis
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OpenAI · 2023
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Measuring the impact of programming language distribution
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Self-consistency improves chain of thought reasoning in language models
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Generating sequences by learning to self-correct
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x, 2023
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Least-to-most prompting enables complex reasoning in large language models
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