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Code understanding and generation have fast become some of the most popular applications of language models (LMs).
Massively multilingual neural machine translation in the wild: Findings and challenges
Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Xu Chen, Yuan Cao, George Foster, Colin Cherry, et al. 2019 · 1907
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On the resemblance and containment of documents
Andrei Z Broder. 1997 · 1997
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LLVM: A compilation framework for lifelong program analysis & transformation
Chris Lattner and Vikram S. Adve. 2004 · 2004
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ORANGE: a method for evaluating automatic evaluation metrics for machine translation
Chin-Yew Lin and Franz Josef Och. 2004 · 2004
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016 · 2016
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The adverse effects of code duplication in machine learning models of code
Miltiadis Allamanis. 2019 · 2019
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Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond
Mikel Artetxe and Holger Schwenk. 2019 · 2019
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Generative code modeling with graphs
Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, and Oleksandr Polozov. 2019 · 2019
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Misleading authorship attribution of source code using adversarial learning
Erwin Quiring, Alwin Maier, and Konrad Rieck. 2019 · 2019
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Corpus building for low resource languages in the DARPA LORELEI program
Jennifer Tracey, Stephanie Strassel, Ann Bies, Zhiyi Song, Michael Arrigo, Kira Griffitt, Dana Delgado, Dave Graff, Seth Kulick, Justin Mott, and Neil Kuster. 2019 · 2019
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Compiler-based graph representations for deep learning models of code
Alexander Brauckmann, Andrés Goens, Sebastian Ertel, and Jeronimo Castrillon. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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From zero to hero: On the limitations of zero-shot language transfer with multilingual Transformers
Anne Lauscher, Vinit Ravishankar, Ivan Vulić, and Goran Glavaš. 2020 · 2020
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Codeforces: Results of 2020 [annual report]
Mike Mirzayanov. 2020 · 2020
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funcgnn: A graph neural network approach to program similarity
Aravind Ashok Nair, Avijit Roy, and Karl Meinke. 2020 · 2020
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Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He. 2020 · 2020
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Treegen: A tree-based transformer architecture for code generation
Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. 2020 · 2020
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On negative interference in multilingual models: Findings and a meta-learning treatment
Zirui Wang, Zachary C. Lipton, and Yulia Tsvetkov. 2020 · 2020
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Are all languages created equal in multilingual BERT?
Shijie Wu and Mark Dredze. 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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Augmentedcode: Examining the effects of natural language resources in code retrieval models
Mehdi Bahrami, NC Shrikanth, Yuji Mizobuchi, Lei Liu, Masahiro Fukuyori, Wei-Peng Chen, and Kazuki Munakata. 2021 · 2021
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Description2code dataset
Ethan Caballero and Ilya Sutskever. 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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Nl-augmenter: A framework for task-sensitive natural language augmentation
Kaustubh D Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Shrivastava, Samson Tan, et al. 2021 · 2021
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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 · 2021
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Contrastive code representation learning
Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph Gonzalez, and Ion Stoica. 2021 · 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 B. 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 · 2021
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Codenet: A large-scale AI for code dataset for learning a diversity of coding tasks
Ruchir Puri, David S. Kung, Geert Janssen, Wei Zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir R. Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, and Frederick Reiss. 2021 · 2021
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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
Cited alongside, same era.
Multilingual training for software engineering
Toufique Ahmed and Premkumar Devanbu. 2022 · 2022
Cited alongside, same era.
Composable sparse fine-tuning for cross-lingual transfer
Alan Ansell, Edoardo Ponti, Anna Korhonen, and Ivan Vulić. 2022 · 2022
Cited alongside, same era.
Multipl-e: A scalable and extensible approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al. 2022 · 2022
Cited alongside, same era.
Natgen: generative pre-training by “naturalizing” source code
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar T Devanbu, and Baishakhi Ray. 2022 · 2022
Cited alongside, same era.
Openassistant conversations - democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and Alexander Mattick. 2023 · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
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Multilingual lottery tickets to pretrain language models
Jaeseong Lee and Seung-won Hwang. 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, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Joel Lamy-Poirier, Joao Monteiro, Nicolas Gontier, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Ben Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason T Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Urvashi Bhattacharyya, Wenhao Yu, Sasha Luccioni, Paulo Villegas, Fedor Zhdanov, Tony Lee, Nadav Timor, Jennifer Ding, Claire S Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro Von Werra, and Harm de Vries. 2023 · 2023
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On the transferability of pre-trained language models for low-resource programming languages
Fuxiang Chen, Fatemeh H. Fard, David Lo, and Timofey Bryksin. 2022 · 2022
Cited alongside, same era.
UniXcoder: Unified cross-modal pre-training for code representation
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022 · 2022
Cited alongside, same era.
An AST structure enhanced decoder for code generation
Hui Jiang, Linfeng Song, Yubin Ge, Fandong Meng, Junfeng Yao, and Jinsong Su. 2022 · 2022
Cited alongside, same era.
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.
Deduplicating training data makes language models better
Katherine Lee, Daphne Ippolito, Andrew Nystrom, Chiyuan Zhang, Douglas Eck, Chris Callison-Burch, and Nicholas Carlini. 2022 · 2022
Cited alongside, same era.
Unleashing the power of compiler intermediate representation to enhance neural program embeddings
Zongjie Li, Pingchuan Ma, Huaijin Wang, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu. 2022 · 2022
Cited alongside, same era.
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Openorca: An open dataset of gpt augmented flan reasoning traces
Wing Lian, Bleys Goodson, Eugene Pentland, Austin Cook, Chanvichet Vong, and "Teknium". 2023 · 2023
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Rltf: Reinforcement learning from unit test feedback
Jiate Liu, Yiqin Zhu, Kaiwen Xiao, Qiang Fu, Xiao Han, Wei Yang, and Deheng Ye. 2023 · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V. Le, Barret Zoph, Jason Wei, and Adam Roberts. 2023 · 2023
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Mulcs: Towards a unified deep representation for multilingual code search
Yingwei Ma, Yue Yu, Shanshan Li, Zhouyang Jia, Jun Ma, Rulin Xu, Wei Dong, and Xiangke Liao. 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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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. 2023 · 2023
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Measuring the impact of programming language distribution
Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, and Michele Catasta. 2023 · 2023
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Openwebmath: An open dataset of high-quality mathematical web text
Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, and Jimmy Ba. 2023 · 2023
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Metatptrans: A meta learning approach for multilingual code representation learning
Weiguo Pian, Hanyu Peng, Xunzhu Tang, Tiezhu Sun, Haoye Tian, Andrew Habib, Jacques Klein, and Tegawendé F. Bissyandé. 2023 · 2023
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Rosetta code
Rosetta Code. 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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Execution-based code generation using deep reinforcement learning
Parshin Shojaee, Aneesh Jain, Sindhu Tipirneni, and Chandan K. Reddy. 2023 · 2023
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peS2o (Pretraining Efficiently on S2ORC) Dataset
Luca Soldaini and Kyle Lo. 2023 · 2023
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Code translation with compiler representations
Marc Szafraniec, Baptiste Rozière, Hugh Leather, Patrick Labatut, François Charton, and Gabriel Synnaeve. 2023 · 2023
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ReCode: Robustness evaluation of code generation models
Shiqi Wang, Zheng Li, Haifeng Qian, Chenghao Yang, Zijian Wang, Mingyue Shang, Varun Kumar, Samson Tan, Baishakhi Ray, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, Dan Roth, and Bing Xiang. 2023 · 2023
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Deceptprompt: Exploiting llm-driven code generation via adversarial natural language instructions
Fangzhou Wu, Xiaogeng Liu, and Chaowei Xiao. 2023 · 2023
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Adversarial robustness of deep code comment generation
Yu Zhou, Xiaoqing Zhang, Juanjuan Shen, Tingting Han, Taolue Chen, and Harald C. Gall. 2022 · 2023
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Breaking the curse of multilinguality with cross-lingual expert language models
Terra Blevins, Tomasz Limisiewicz, Suchin Gururangan, Margaret Li, Hila Gonen, Noah A Smith, and Luke Zettlemoyer. 2024 · 2024
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Multi-line ai-assisted code authoring
Omer Dunay, Daniel Cheng, Adam Tait, Parth Thakkar, Peter C Rigby, Andy Chiu, Imad Ahmad, Arun Ganesan, Chandra Maddila, Vijayaraghavan Murali, et al. 2024 · 2024
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Resolving code review comments with machine learning
Alexander Frömmgen, Jacob Austin, Peter Choy, Nimesh Ghelani, Lera Kharatyan, Gabriela Surita, Elena Khrapko, Pascal Lamblin, Pierre-Antoine Manzagol, Marcus Revaj, Maxim Tabachnyk, Daniel Tarlow, Kevin Villela, Dan Zheng, Satish Chandra, and Petros Maniatis. 2024 · 2024
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Ast-t5: Structure-aware pretraining for code generation and understanding
Linyuan Gong, Mostafa Elhoushi, and Alvin Cheung. 2024 · 2024
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Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y Wu, YK Li, et al. 2024 · 2024
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Do large code models understand programming concepts? a black-box approach
Ashish Hooda, Mihai Christodorescu, Miltos Allamanis, Aaron Wilson, Kassem Fawaz, and Somesh Jha. 2024 · 2024
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Structcoder: Structure-aware transformer for code generation
Sindhu Tipirneni, Ming Zhu, and Chandan K. Reddy. 2024 · 2024
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