Fetching the paper…
Reading the bibliography…
Currently, large pre-trained language models are widely applied in neural code completion systems.
Determining Sample Size for Research Activities
Robert V. Krejcie and Daryle W. Morgan. 1970 · 1970
Earlier work this paper cites.
Automatic parameter selection by minimizing estimated error
Ron Kohavi and George H John. 1995 · 1995
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory
Yoav Freund and Robert E. Schapire. 1997 · 1997
Earlier work this paper cites.
Prediction error estimation methods
Lennart Ljung. 2002 · 2002
Earlier work this paper cites.
Bleu: a Method for Automatic Evaluation of Machine Translation. In ACL
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation. In COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics . COLING, Geneva, Switzerland, 501–507
Chin-Yew Lin and Franz Josef Och. 2004 · 2004
Earlier work this paper cites.
Prediction error estimation: a comparison of resampling methods
Annette M Molinaro, Richard Simon, and Ruth M Pfeiffer. 2005 · 2005
Earlier work this paper cites.
Understanding the test automation culture of app developers. In 2015 IEEE 8th International Conference on Software Testing, Verification and Validation (ICST) . IEEE, 1–10
Pavneet Singh Kochhar, Ferdian Thung, Nachiappan Nagappan, Thomas Zimmermann, and David Lo. 2015 · 2015
Earlier work this paper cites.
Compression of Neural Machine Translation Models via Pruning. In CoNLL
A. See, Minh-Thang Luong, and Christopher D. Manning. 2016 · 2016
Earlier work this paper cites.
Neural Machine Translation of Rare Words with Subword Units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
Earlier work this paper cites.
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Deep code search. In Proceedings of the 40th International Conference on Software Engineering . 933–944
Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018 · 2018
Earlier work this paper cites.
Deep Code Comment Generation
Xing Hu, Ge Li, Xin Xia, D. Lo, and Zhi Jin. 2018 · 2018
Earlier work this paper cites.
Code Completion with Neural Attention and Pointer Networks
Jian Li, Yue Wang, Michael R. Lyu, and Irwin King. 2018 · 2018
Earlier work this paper cites.
Scaling Video Analytics on Constrained Edge Nodes
Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky, and Subramanya R. Dulloor. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In North American Chapter of the Association for Computational Linguistics
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Hongqi Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019 · 2019
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Energy and Policy Considerations for Deep Learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
Cited alongside, same era.
Pythia: AI-assisted Code Completion System
Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, and Neel Sundaresan. 2019 · 2019
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2020 · 2020
Cited alongside, same era.
Predicting Performance for Natural Language Processing Tasks
M. Xia, Antonios Anastasopoulos, Ruochen Xu, Yiming Yang, and Graham Neubig. 2020 · 2020
ML-powered coding companion – Amazon CodeWhisperer – Amazon Web Services
2022 · 2022
Closest in time.
Grounded Copilot: How Programmers Interact with Code-Generating Models
Shraddha Barke, Michael B. James, and Nadia Polikarpova. 2022 · 2022
Closest in time.
Efficient training of language models to fill in the middle
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022 · 2022
Closest in time.
CrystalBLEU: Precisely and Efficiently Measuring the Similarity of Code
Aryaz Eghbali and Michael Pradel. 2022 · 2022
Closest in time.
Out of the BLEU: how should we assess quality of the Code Generation models?
Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov, and Timofey Bryksin. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Efficient Neural Architecture Search with Performance Prediction
Ibrahim Alshubaily. 2021 · 2021
Cited alongside, same era.
Evaluating Large Language Models Trained on Code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, David W. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, Igor Babuschkin, S. Arun Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew M. Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Cited alongside, same era.
Compute and Energy Consumption Trends in Deep Learning Inference
Radosvet Desislavov, Fernando Mart’inez-Plumed, and Jos’e Hern’andez-Orallo. 2021 · 2021
Cited alongside, same era.
Knowledge Distillation: A Survey
Jianping Gou, B. Yu, Stephen J. Maybank, and Dacheng Tao. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Reassessing automatic evaluation metrics for code summarization tasks
Devjeet Roy, Sarah Fakhoury, and Venera Arnaoudova. 2021 · 2021
Cited alongside, same era.
Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
Cited alongside, same era.
Closest in time.
Competition-Level Code Generation with AlphaCode
Yujia Li, David H. 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 Jaymin Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de, Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022
Closest in time.
A Conversational Paradigm for Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Haiquan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022b · 2022
Closest in time.
On the Importance of Building High-quality Training Datasets for Neural Code Search
Zhensu Sun, Li Li, Y. Liu, and Xiaoning Du. 2022 · 2022
Closest in time.
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In CHI Conference on Human Factors in Computing Systems Extended Abstracts . 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
Closest in time.
Natural Attack for Pre-trained Models of Code
Zhou Yang, Jieke Shi, Junda He, and David Lo. 2022 · 2022
Closest in time.
Productivity Assessment of Neural Code Completion
Albert Ziegler, Eirini Kalliamvakou, Shawn Simister, Ganesh Sittampalam, Alice Li, Andrew SC Rice, Devon Rifkin, and Edward Aftandilian. 2022 · 2022
Closest in time.
AlphaCode 2 Technical Report
Google DeepMind AlphaCode Team. 2023 · 2023
Closest in time.
Large Language Models for Software Engineering: A Systematic Literature Review
Xinying Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John C. Grundy, and Haoyu Wang. 2023 · 2023
Closest in time.
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, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nourhan Fahmy, Urvashi Bhattacharyya, W. Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jana Ebert, Tri Dao, Mayank Mishra, Alexander Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean M. Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. 2023 · 2023
Closest in time.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023 · 2023
Closest in time.
CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp, Hiroaki Hayashi, Caiming Xiong, Silvio Savarese, and Yingbo Zhou. 2023 · 2023
Closest in time.
A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends
Zibin Zheng, Kaiwen Ning, Yanlin Wang, Jingwen Zhang, Dewu Zheng, Mingxi Ye, and Jiachi Chen. 2023 · 2023
Closest in time.
Stealthy backdoor attack for code models
Zhou Yang, Bowen Xu, Jie M Zhang, Hong Jin Kang, Jieke Shi, Junda He, and David Lo. 2024 · 2024
Closest in time.