Fetching the paper…
Reading the bibliography…
Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the context present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby producing inaccurate code completions.
A probabilistic model of information retrieval: development and comparative experiments - part 1
Karen Sparck Jones, Steve Walker, and Stephen E. Robertson · 2000
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Are deep neural networks the best choice for modeling source code?
Vincent J. Hellendoorn and Premkumar Devanbu · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
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, et al · 2020
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2020
Earlier work this paper cites.
On-the-fly adaptation of source code models
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2020
Earlier work this paper cites.
Intellicode compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2021
Earlier work this paper cites.
Embedding api dependency graph for neural code generation
Chen Lyu, Ruyun Wang, Hongyu Zhang, Hanwen Zhang, and Songlin Hu · 2021
Earlier work this paper cites.
Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
Earlier work this paper cites.
An empirical cybersecurity evaluation of github copilot’s code contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri · 2021
Earlier work this paper cites.
CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation
Yue Wang, Weishi Wang, Shafiq Joty, and Steven C.H. Hoi · 2021
Cited alongside, same era.
Learning to generate code comments from class hierarchies, 2021
Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Raymond J. Mooney, Junyi Jessy Li, and Milos Gligoric · 2021
Cited alongside, same era.
How readable is model-generated code? examining readability and visual inspection of github copilot
Naser Al Madi · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Taking flight with copilot: Early insights and opportunities of ai-powered pair-programming tools
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit · 2022
Reading between the lines: Modeling user behavior and costs in ai-assisted programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz · 2022
Later among the works it cites.
Do users write more insecure code with ai assistants?
Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh · 2022
Later among the works it cites.
Repository-level prompt generation for large language models of code
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2022
Later among the works it cites.
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman · 2022
Later among the works it cites.
Capturing structural locality in non-parametric language models
Frank F. Xu, Junxian He, Graham Neubig, and Vincent Josua Hellendoorn · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
Cited alongside, same era.
Fido: Fusion-in-decoder optimized for stronger performance and faster inference
Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit K. Sanghai, Fei Sha, and William Cohen · 2022
Cited alongside, same era.
Cocomic: Code completion by jointly modeling in-file and cross-file context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang · 2022
Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis, 2022
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer, and Mike Lewis · 2022
Cited alongside, same era.
The stack: 3 tb of permissively licensed source code
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou, Carlos Muñoz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes, Thomas Wolf, et al · 2022
Cited alongside, same era.
Competition-level code generation with alphacode, 2022
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 · 2022
Cited alongside, same era.
Reacc: A retrieval-augmented code completion framework
Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy · 2022
Cited alongside, same era.
Later among the works it cites.
When language model meets private library
Daoguang Zan, Bei Chen, Zeqi Lin, Bei Guan, Wang Yongji, and Jian-Guang Lou · 2022
Later among the works it cites.
Santacoder: don’t reach for the stars!
Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, et al · 2023
Closest in time.
Grounded copilot: How programmers interact with code-generating models
Shraddha Barke, Michael B James, and Nadia Polikarpova · 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, et al · 2023
Closest in time.
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
Closest in time.
Repocoder: Repository-level code completion through iterative retrieval and generation
Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen · 2023
Closest in time.
Docprompting: Generating code by retrieving the docs
Shuyan Zhou, Uri Alon, Frank F. Xu, Zhengbao Jiang, and Graham Neubig · 2023
Closest in time.