Mask-Predict: Parallel Decoding of Conditional Masked Language Models
Original
Marjan Ghazvininejad, Omer Levy, Yinhan Liu, and Luke Zettlemoyer. 2019 · 1904
Earlier work this paper cites.
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Original
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Sequence Model Design for Code Completion in the Modern IDE
Original
Gareth Ari Aye and Gail E. Kaiser. 2020 · 2004
Earlier work this paper cites.
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Original
Gautier Izacard and Edouard Grave. 2021 · 2007
Earlier work this paper cites.
Assessing ICT global emissions footprint: Trends to 2040 & recommendations
Lotfi Belkhir and Ahmed Elmeligi. 2018 · 2017
Earlier work this paper cites.
Attention is All you Need. In Advances in Neural Information Processing Systems , I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Non-Autoregressive Neural Machine Translation. In International Conference on Learning Representations
Jiatao Gu, James Bradbury, Caiming Xiong, Victor O.K. Li, and Richard Socher. 2018 · 2018
Earlier work this paper cites.
SPT-code: sequence-to-sequence pre-training for learning source code representations. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 2006–2018
Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo. 2022 · 2018
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , Jill Burstein, Christy Doran, and Thamar Solorio (Eds.). Association for Computational Linguistics, Minneapolis, Minnesota, 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Language Models are Few-Shot Learners. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 1877–1901
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 · 2020
Earlier work this paper cites.
Certifying a rule-based model transformation engine for proof preservation. In Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (Virtual Event, Canada) (MODELS ’20) . Association for Computing Machinery, New York, NY, USA, 297–307
Zheng Cheng, Massimo Tisi, and Joachim Hotonnier. 2020 · 2020
Earlier work this paper cites.
CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 , Trevor Cohn, Yulan He, and Yang Liu (Eds.). Association for Computational Linguistics, Online, 1536–1547
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020 · 2020
Earlier work this paper cites.
Learning and Evaluating Contextual Embedding of Source Code. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 5110–5121
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020 · 2020
Earlier work this paper cites.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 9459–9474
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
Earlier work this paper cites.
REFINITY to Model and Prove Program Transformation Rules. In Programming Languages and Systems , Bruno C. d. S. Oliveira (Ed.). Springer International Publishing, Cham, 311–319
Dominic Steinhöfel. 2020 · 2020
Earlier work this paper cites.
IntelliCode compose: code generation using transformer. In Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Virtual Event, USA) (ESEC/FSE 2020) . Association for Computing Machinery, New York, NY, USA, 1433–1443
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020 · 2020
Earlier work this paper cites.
Code and Named Entity Recognition in StackOverflow. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , Dan Jurafsky, Joyce Chai, Natalie Schluter, and Joel Tetreault (Eds.). Association for Computational Linguistics, Online, 4913–4926
Jeniya Tabassum, Mounica Maddela, Wei Xu, and Alan Ritter. 2020 · 2020
Earlier work this paper cites.
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes. In International Conference on Learning Representations
Yang You, Jing Li, Sashank Reddi, Jonathan Hseu, Sanjiv Kumar, Srinadh Bhojanapalli, Xiaodan Song, James Demmel, Kurt Keutzer, and Cho-Jui Hsieh. 2020 · 2020
Earlier work this paper cites.
Unified Pre-training for Program Understanding and Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (Eds.). Association for Computational Linguistics, Online, 2655–2668
Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Earlier work this paper cites.
Efficient Compiler Autotuning via Bayesian Optimization. In 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE) . 1198–1209
Junjie Chen, Ningxin Xu, Peiqi Chen, and Hongyu Zhang. 2021b · 2021
Earlier work this paper cites.
GraphCode{BERT}: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations
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 · 2021
Earlier work this paper cites.
CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)
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 · 2021
Earlier work this paper cites.
Retrieval Augmented Code Generation and Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021 , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Punta Cana, Dominican Republic, 2719–2734
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Earlier work this paper cites.
Carbon Emissions and Large Neural Network Training
Original
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. 2021 · 2021
Earlier work this paper cites.
CoTexT: Multi-task Learning with Code-Text Transformer. In Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021) , Royi Lachmy, Ziyu Yao, Greg Durrett, Milos Gligoric, Junyi Jessy Li, Ray Mooney, Graham Neubig, Yu Su, Huan Sun, and Reut Tsarfaty (Eds.). Association for Computational Linguistics, Online, 40–47
Long Phan, Hieu Tran, Daniel Le, Hieu Nguyen, James Annibal, Alec Peltekian, and Yanfang Ye. 2021 · 2021
Earlier work this paper cites.
Fast and Memory-Efficient Neural Code Completion. In 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR) . 329–340
Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, and Miltiadis Allamanis. 2021 · 2021
Earlier work this paper cites.
Technologies and perspectives for achieving carbon neutrality
Fang Wang, Jean Damascene Harindintwali, Zhizhang Yuan, Min Wang, Faming Wang, Sheng Li, Zhigang Yin, Lei Huang, Yuhao Fu, Lei Li, Scott X. Chang, Linjuan Zhang, Jörg Rinklebe, Zuoqiang Yuan, Qinggong Zhu, Leilei Xiang, Daniel C.W. Tsang, Liang Xu, Xin Jiang, Jihua Liu, Ning Wei, Matthias Kästner, Yang Zou, Yong Sik Ok, Jianlin Shen, Dailiang Peng, Wei Zhang, Damià Barceló, Yongjin Zhou, Zhaohai Bai, Boqiang Li, Bin Zhang, Ke Wei, Hujun Cao, Zhiliang Tan, Liu bin Zhao, Xiao He, Jinxing Zheng, Nanthi Bolan, Xiaohong Liu, Changping Huang, Sabine Dietmann, Ming Luo, Nannan Sun, Jirui Gong, Yulie Gong, Ferdi Brahushi, Tangtang Zhang, Cunde Xiao, Xianfeng Li, Wenfu Chen, Nianzhi Jiao, Johannes Lehmann, Yong-Guan Zhu, Hongguang Jin, Andreas Schäffer, James M. Tiedje, and Jing M. Chen. 2021a · 2021
Earlier work this paper cites.
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 8696–8708
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi. 2021b · 2021
Earlier work this paper cites.
Parameter-Efficient Finetuning of Transformers for Source Code
Original
Shamil Ayupov and Nadezhda Chirkova. 2022 · 2022
Earlier work this paper cites.
NatGen: generative pre-training by “naturalizing” source code. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Singapore, Singapore) (ESEC/FSE 2022) . Association for Computing Machinery, New York, NY, USA, 18–30
Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar T. Devanbu, and Baishakhi Ray. 2022 · 2022
Earlier work this paper cites.
Accelerating Code Search with Deep Hashing and Code Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 2534–2544
Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Michael Lyu. 2022 · 2022
Earlier work this paper cites.
UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , Smaranda Muresan, Preslav Nakov, and Aline Villavicencio (Eds.). Association for Computational Linguistics, Dublin, Ireland, 7212–7225
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
Earlier work this paper cites.
An empirical analysis of compute-optimal large language model training. In Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35. Curran Associates, Inc., 30016–30030
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Thomas Hennigan, Eric Noland, Katherine Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karén Simonyan, Erich Elsen, Oriol Vinyals, Jack Rae, and Laurent Sifre. 2022 · 2022
Earlier work this paper cites.
CodeRetriever: A Large Scale Contrastive Pre-Training Method for Code Search. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (Eds.). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, 2898–2910
Xiaonan Li, Yeyun Gong, Yelong Shen, Xipeng Qiu, Hang Zhang, Bolun Yao, Weizhen Qi, Daxin Jiang, Weizhu Chen, and Nan Duan. 2022b · 2022
Earlier work this paper cites.
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, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022a · 2022
Earlier work this paper cites.
Can Identifier Splitting Improve Open-Vocabulary Language Model of Code?. In 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) . 1134–1138
Jieke Shi, Zhou Yang, Junda He, Bowen Xu, and David Lo. 2022 · 2022
Earlier work this paper cites.
Transformer-Based Language Models for Software Vulnerability Detection. In Proceedings of the 38th Annual Computer Security Applications Conference (Austin, TX, USA) (ACSAC ’22) . Association for Computing Machinery, New York, NY, USA, 481–496
Chandra Thapa, Seung Ick Jang, Muhammad Ejaz Ahmed, Seyit Camtepe, Josef Pieprzyk, and Surya Nepal. 2022 · 2022
Earlier work this paper cites.
Finding missed optimizations through the lens of dead code elimination. In Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (Lausanne, Switzerland) (ASPLOS ’22) . Association for Computing Machinery, New York, NY, USA, 697–709
Theodoros Theodoridis, Manuel Rigger, and Zhendong Su. 2022 · 2022
Earlier work this paper cites.
Bridging pre-trained models and downstream tasks for source code understanding. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . Association for Computing Machinery, New York, NY, USA, 287–298
Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao. 2022 · 2022
Earlier work this paper cites.
A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (San Diego, CA, USA) (MAPS 2022) . Association for Computing Machinery, New York, NY, USA, 1–10
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Earlier work this paper cites.
Diet Code is Healthy: Simplifying Programs for Pre-Trained Models of Code. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Singapore, Singapore) (ESEC/FSE 2022) . Association for Computing Machinery, New York, NY, USA, 1073–1084
Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022 · 2022
Earlier work this paper cites.
Exploring and Evaluating Personalized Models for Code Generation (ESEC/FSE 2022) . 1500–1508
Andrei Zlotchevski, Dawn Drain, Alexey Svyatkovskiy, Colin B. Clement, Neel Sundaresan, and Michele Tufano. 2022 · 2022
Earlier work this paper cites.
Few-shot training LLMs for project-specific code-summarization. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering (Rochester, MI, USA) (ASE ’22) . Association for Computing Machinery, New York, NY, USA, Article 177, 5 pages
Toufique Ahmed and Premkumar Devanbu. 2023 · 2023
Earlier work this paper cites.
Accelerating Large Language Model Decoding with Speculative Sampling
Original
Charlie Chen, Sebastian Borgeaud, Geoffrey Irving, Jean-Baptiste Lespiau, Laurent Sifre, and John Jumper. 2023 · 2023
Earlier work this paper cites.
The growing energy footprint of artificial intelligence
Alex de Vries. 2023 · 2023
Earlier work this paper cites.
Evaluating the Carbon Impact of Large Language Models at the Inference Stage. In 2023 IEEE International Performance, Computing, and Communications Conference (IPCCC) . 150–157
Brad Everman, Trevor Villwock, Dayuan Chen, Noe Soto, Oliver Zhang, and Ziliang Zong. 2023 · 2023
Earlier work this paper cites.
Large Language Models for Software Engineering: Survey and Open Problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software Engineering (ICSE-FoSE) . 31–53
Angela Fan, Beliz Gokkaya, Mark Harman, Mitya Lyubarskiy, Shubho Sengupta, Shin Yoo, and Jie M. Zhang. 2023 · 2023
Earlier work this paper cites.