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Using large language models (LLMs) for source code has recently gained attention.
BLEU: A method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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Aizu Online Judge, 2004
Yutaka Watanobe · 2004
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A Universally Unique IDentifier (UUID) URN Namespace
Paul J. Leach, Rich Salz, and Michael H. Mealling · 2005
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Improving neural machine translation models with monolingual data
Rico Sennrich, Barry Haddow, and Alexandra Birch · 2016
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Automatic algorithm recognition of source-code using machine learning
Maged Shalaby, Tarek Mehrez, Amr El Mougy, Khalid Abdulnasser, and Aysha Al-Safty · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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DeepCoder: Learning to write programs
Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2017
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A survey on online judge systems and their applications
Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, and Tomasz Sternal · 2018
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Code generation as a dual task of code summarization
Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, and Zhi Jin · 2019
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Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
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IntelliCode Compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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The Pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2020
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CodeBLEU: A method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, Neel Sundaresan, Ming Zhou, Ambrosio Blanco, and Shuai Ma · 2020
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CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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CodeXGLUE: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin Clement, Dawn Drain, Daxin Jiang, Duyu Tang, Ge Li, Lidong Zhou, Linjun Shou, Long Zhou, Michele Tufano, MING GONG, Ming Zhou, Nan Duan, Neel Sundaresan, Shao Kun Deng, Shengyu Fu, and Shujie LIU · 2021
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Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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Code completion for programming education based on deep learning
Kenta Terada and Yutaka Watanobe · 2021
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Tfix: Learning to fix coding errors with a text-to-text transformer
Berkay Berabi, Jingxuan He, Veselin Raychev, and Martin Vechev · 2021
Cited alongside, same era.
A bidirectional lstm language model for code evaluation and repair
Md. Mostafizer Rahman, Yutaka Watanobe, and Keita Nakamura · 2021
Cited alongside, same era.
A model with iterative trials for correcting logic errors in source code
Taku Matsumoto, Yutaka Watanobe, and Keita Nakamura · 2021
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramèr, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Úlfar Erlingsson, Alina Oprea, and Colin Raffel · 2021
Cited alongside, same era.
Memorization vs. generalization : Quantifying data leakage in NLP performance evaluation
Aparna Elangovan, Jiayuan He, and Karin Verspoor · 2021
Cited alongside, same era.
Measuring coding challenge competence with APPS
To what extent do deep learning-based code recommenders generate predictions by cloning code from the training set?
Matteo Ciniselli, Luca Pascarella, and Gabriele Bavota · 2022
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Codex hacks hackerrank: Memorization issues and a framework for code synthesis evaluation
Anjan Karmakar, Julian Aron Prenner, Marco D’Ambros, and Romain Robbes · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe · 2022
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Online judge system: Requirements, architecture, and experiences
Yutaka Watanobe, Md. Mostafizer Rahman, Taku Matsumoto, Uday Kiran Rage, and Penugonda Ravikumar · 2022
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Solving probability and statistics problems by probabilistic program synthesis at human level and predicting solvability
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Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt · 2021
Cited alongside, same era.
Solving linear algebra by program synthesis
Iddo Drori and Nakul Verma · 2021
Cited alongside, same era.
TreeBERT: A tree-based pre-trained model for programming language
Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, and Lei Lyu · 2021
Cited alongside, same era.
GraphCodeBERT: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou · 2021
Cited alongside, same era.
Studying the usage of text-to-text transfer transformer to support code-related tasks
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota · 2021
Cited alongside, same era.
CoTexT: Multi-task learning with code-text transformer
Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Annibal, Alec Peltekian, and Yanfang Ye · 2021
Cited alongside, same era.
CodeNet: A large-scale ai for code dataset for learning a diversity of coding tasks
Ruchir Puri, David Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian T Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss · 2021
Cited alongside, same era.
Leonard Tang, Elizabeth Ke, Nikhil Singh, Bo Feng, Derek Austin, Nakul Verma, and Iddo Drori · 2022
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A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level
Iddo Drori, Sarah Zhang, Reece Shuttleworth, Leonard Tang, Albert Lu, Elizabeth Ke, Kevin Liu, Linda Chen, Sunny Tran, Newman Cheng, Roman Wang, Nikhil Singh, Taylor L. Patti, Jayson Lynch, Avi Shporer, Nakul Verma, Eugene Wu, and Gilbert Strang · 2022
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Automatic generation of programming exercises and code explanations using large language models
Sami Sarsa, Paul Denny, Arto Hellas, and Juho Leinonen · 2022
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Asleep at the keyboard? assessing the security of github copilot’s code contributions
H. Pearce, B. Ahmad, B. Tan, B. Dolan-Gavitt, and R. Karri · 2022
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Probing pretrained models of source codes
Sergey Troshin and Nadezhda Chirkova · 2022
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Code generation for unknown libraries via reading api documentations
Koki Washio and Yusuke Miyao · 2022
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Natural language to code translation with execution
Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I. Wang · 2022
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UniXcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin · 2022
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The robots are coming: Exploring the implications of openai codex on introductory programming
James Finnie-Ansley, Paul Denny, Brett A. Becker, Andrew Luxton-Reilly, and James Prather · 2022
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Less is more: Summary of long instructions is better for program synthesis
Kirby Kuznia, Swaroop Mishra, Mihir Parmar, and Chitta Baral · 2022
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Code summarization: Do transformers really understand code?
Ankita Nandkishor Sontakke, Manasi Patwardhan, Lovekesh Vig, Raveendra Kumar Medicherla, Ravindra Naik, and Gautam Shroff · 2022
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DS-1000: A natural and reliable benchmark for data science code generation
Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen tau Yih, Daniel Fried, Sida Wang, and Tao Yu · 2022
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A simple, yet effective approach to finding biases in code generation
Spyridon Mouselinos, Mateusz Malinowski, and Henryk Michalewski · 2022
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InCoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis · 2023
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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 · 2023
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Examining zero-shot vulnerability repair with large language models
H. Pearce, B. Tan, B. Ahmad, R. Karri, and B. Dolan-Gavitt · 2023
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Identifying algorithm in program code based on structural features using cnn classification model
Yutaka Watanobe, Md Mostafizer Rahman, Md Faizul Ibne Amin, and Raihan Kabir · 2023
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On the robustness of code generation techniques: An empirical study on github copilot
Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota · 2023
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OpenAI · 2023
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DocPrompting: Generating code by retrieving the docs
Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao Jiang, and Graham Neubig · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed Chi, Nathanael Schärli, and Denny Zhou · 2023
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