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Large language models (LLMs) are leading significant progress in code generation.
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Frances E Allen. 1970 · 1970
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John Cocke. 1970 · 1970
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Thomas Ball and James R Larus. 1996 · 1996
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Glenn Ammons and James R Larus. 1998 · 1998
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James R Larus. 1999 · 1999
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pdb — the python debugger
Python Software Foundation. 2001 · 2001
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Basic block distribution analysis to find periodic behavior and simulation points in applications
Timothy Sherwood, Erez Perelman, and Brad Calder. 2001 · 2001
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Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav. 2014 · 2014
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A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig. 2017 · 2017
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Execution-guided neural program synthesis
Xinyun Chen, Chang Liu, and Dawn Song. 2018 · 2018
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Code completion with neural attention and pointer networks
Jian Li, Yue Wang, Michael R. Lyu, and Irwin King. 2018 · 2018
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
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Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021
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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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{ \{ PYLIVE } \} : { \{ On-the-Fly } \} code change for python-based online services
Haochen Huang, Chengcheng Xiang, Li Zhong, and Yuanyuan Zhou. 2021 · 2021
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Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2022 · 2022
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Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, et al. 2022 · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. 2022 · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al. 2022 · 2022
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Learning math reasoning from self-sampled correct and partially-correct solutions
Ansong Ni, Jeevana Priya Inala, Chenglong Wang, Alex Polozov, Christopher Meek, Dragomir Radev, and Jianfeng Gao. 2022 · 2022
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Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, and Shafiq Joty. 2023 · 2023
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Starcoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. 2023 · 2023
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Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Natural language to code translation with execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I Wang. 2022 · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
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, et al. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Cited alongside, same era.
Automated repair of programs from large language models
Zhiyu Fan, Xiang Gao, Martin Mirchev, Abhik Roychoudhury, and Shin Hwei Tan. 2023 · 2023
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Octopack: Instruction tuning code large language models
Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro Von Werra, and Shayne Longpre. 2023 · 2023
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Lever: Learning to verify language-to-code generation with execution
Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. 2023 · 2023
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Gorilla: Large language model connected with massive apis
Shishir G Patil, Tianjun Zhang, Xin Wang, and Joseph E Gonzalez. 2023 · 2023
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Examining zero-shot vulnerability repair with large language models
Hammond Pearce, Benjamin Tan, Baleegh Ahmad, Ramesh Karri, and Brendan Dolan-Gavitt. 2023 · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. 2023 · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao. 2023 · 2023
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Explain-then-translate: an analysis on improving program translation with self-generated explanations
Zilu Tang, Mayank Agarwal, Alexander Shypula, Bailin Wang, Derry Wijaya, Jie Chen, and Yoon Kim. 2023 · 2023
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Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2023 · 2023
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Evaluating instruction-tuned large language models on code comprehension and generation
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, Mingwei Liu, Xin Peng, and Yiling Lou. 2023 · 2023
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A survey of prevent and detect access control vulnerabilities
Li Zhong. 2023 · 2023
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A study on robustness and reliability of large language model code generation
Li Zhong and Zilong Wang. 2023 · 2023
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Leveraging print debugging to improve code generation in large language models
Xueyu Hu, Kun Kuang, Jiankai Sun, Hongxia Yang, and Fei Wu. 2024 · 2024
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Codepori: Large scale model for autonomous software development by using multi-agents
Zeeshan Rasheed, Muhammad Waseem, Mika Saari, Kari Systä, and Pekka Abrahamsson. 2024 · 2024
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Chain-of-table: Evolving tables in the reasoning chain for table understanding
Zilong Wang, Hao Zhang, Chun-Liang Li, Julian Martin Eisenschlos, Vincent Perot, Zifeng Wang, Lesly Miculicich, Yasuhisa Fujii, Jingbo Shang, Chen-Yu Lee, et al. 2024 · 2024
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Effective bug detection with unused definitions
Li Zhong, Chengcheng Xiang, Haochen Huang, Bingyu Shen, Eric Mugnier, and Yuanyuan Zhou. 2024 · 2024
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