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Large language models (LLMs) such as GPT-3.5 and CodeLlama are powerful models for code generation and understanding.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam Girish Sastry, Amanda Askell, Sandhini Agarwa, l Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Clemens Winter Jeffrey Wu, Christopher 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 · 2020
Earlier work this paper cites.
Codebert: A pretrained model for programming and natural languages
Z. Feng, D.Guo, D. Tang, N. Duan, X. Feng, M. Gong, L. Shou, B. Qin, T. Liu, and D. Jiang. 2020 · 2020
Earlier work this paper cites.
Global relational models of source code
Vincent J. Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber. 2020 · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Unified pre-training for program understanding and generation
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021a · 2021
Earlier work this paper cites.
Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, W. Wang, S. Joty, and Steven CH Hoi. 2021 · 2021
Earlier work this paper cites.
Self-supervised bug detection and repair
M. Allamanis, H.Jackson-Flux, and M. Brockschmidt. 2022 · 2022
Cited alongside, same era.
Deep learning based vulnerability detection: Are we there yet?
Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2022 · 2022
Cited alongside, same era.
Plur: A unifying, graph-based view of program learning, understanding, and repair
Zimin Chen, Vincent J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, and Subhodeep Moitra. 2022 · 2022
Cited alongside, same era.
Vulrepair: A t5-based automated software vulnerability repair
Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, and Dinh Phung. 2022 · 2022
Cited alongside, same era.
Better patching using llm prompting, via self-consistency
Toufique Ahmed and Premkumar Devanbu. 2023 · 2023
Cited alongside, same era.
Pal: Program-aided language models
Luyu Gao, Aman Madaan, Shuyan Zhou, Uri Alon, Pengfei Liu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
Closest in time.
Rethinking negative pairs in code search
Haochen Li, Zhou, Xin, Tuan, Luu Anh, Miao, and Chunyan. 2023 · 2023
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2023 · 2023
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Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve. 2023 · 2023
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Kamel Alrashedy, Vincent J. Hellendoorn, and Alessandro Orso. 2023 · 2023
Cited alongside, same era.
Unified pre-training for program understanding and generation
Wasi Uddin Ahmad, S. Chakraborty, B. Ray, and K. Chang. 2021b
Cited in the paper.
Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks
Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2023a
Cited in the paper.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou1. 2023b
Cited in the paper.
Learning performance-improving code edits
Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, and Amir Yazdanbakhsh. 2023a
Cited in the paper.
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023b
Cited in the paper.
Shuyan Zhou, Uri Alon, Frank F. Xu, Zhiruo Wang, Zhengbao Jiang, and Graham Neubig. 2023 · 2023
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Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning
Chaozheng Zongjie Li Wang, Yun Peng, Shuzheng Gao, Sirong Chen, Shuai Wang, Cuiyun Gao, and Michael R. Lyu. 2024 · 2024
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