Fetching the paper…
Reading the bibliography…
Code generation models can benefit data scientists' productivity by automatically generating code from context and text descriptions.
Language models are few-shot learners
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 · 1901
Earlier work this paper cites.
Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, and Jasmine Wang. 2019 · 1908
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 · 1909
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, M. Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2009
Earlier work this paper cites.
Learning to generate pseudo-code from source code using statistical machine translation (t)
Yusuke Oda, Hiroyuki Fudaba, Graham Neubig, Hideaki Hata, Sakriani Sakti, Tomoki Toda, and Satoshi Nakamura. 2015 · 2015
Earlier work this paper cites.
Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Jupyter notebooks - a publishing format for reproducible computational workflows
Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian E. Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica B. Hamrick, Jason Grout, Sylvain Corlay, Paul Ivanov, Damián Avila, Safia Abdalla, Carol Willing, and Jupyter Development Team. 2016 · 2016
Earlier work this paper cites.
50 years of data science
David L. Donoho. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
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.
Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher. 2017 · 2017
Earlier work this paper cites.
Mapping language to code in programmatic context
Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system
Xi Victoria Lin, Chenglong Wang, Luke Zettlemoyer, and Michael D. Ernst. 2018 · 2018
Cited alongside, same era.
Neural program search: Solving data processing tasks from description and examples
Illia Polosukhin and Alexander Skidanov. 2018 · 2018
Cited alongside, same era.
Learning to mine aligned code and natural language pairs from stack overflow
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig. 2018 · 2018
Cited alongside, same era.
Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai-Chou Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Z Li, Qingning Yao, Shanelle Roman, Zilin Zhang, and Dragomir R. Radev. 2018 · 2018
Cited alongside, same era.
Auto-suggest: Learning-to-recommend data preparation steps using data science notebooks
Cong Yan and Yeye He. 2020 · 2020
Later among the works it cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021 · 2021
Later among the works it cites.
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
Later among the works it cites.
The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Rose Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2021 · 2021
Later among the works it cites.
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
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Naps: Natural program synthesis dataset
Maksym Zavershynskyi, Alexander Skidanov, and Illia Polosukhin. 2018 · 2018
Cited alongside, same era.
JuICe: A large scale distantly supervised dataset for open domain context-based code generation
Rajas Agashe, Srinivasan Iyer, and Luke Zettlemoyer. 2019 · 2019
Cited alongside, same era.
Spoc: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alexander Aiken, and Percy Liang. 2019 · 2019
Cited alongside, same era.
PyMT5: multi-mode translation of natural language and python code with transformers
Colin Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan. 2020 · 2020
Cited alongside, same era.
Deep learning for source code modeling and generation
Triet Huynh Minh Le, Hao Chen, and Muhammad Ali Babar. 2020 · 2020
Cited alongside, same era.
Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020 · 2020
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. 2021a
Cited in the paper.
Later among the works it cites.
HAConvGNN: Hierarchical attention based convolutional graph neural network for code documentation generation in Jupyter notebooks
Xuye Liu, Dakuo Wang, April Wang, Yufang Hou, and Lingfei Wu. 2021 · 2021
Later among the works it cites.
CodeXGLUE: A machine learning benchmark dataset for code understanding and generation
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
Later among the works it cites.
Graph-augmented code summarization in computational notebooks
April Yi Wang, Dakuo Wang, Xuye Liu, and Lingfei Wu. 2021 · 2021
Later among the works it cites.
A survey on artificial intelligence for source code: A dialogue systems perspective
Erfan Al-Hossami and Samira Shaikh. 2022 · 2022
Closest in time.
Training and evaluating a jupyter notebook data science assistant
Shubham Chandel, Colin B. Clement, Guillermo Serrato, and Neel Sundaresan. 2022 · 2022
Closest in time.
Natural Language Processing with Transformers: Building Language Applications with Hugging Face
Lewis Tunstall, Leandro von Werra, and Thomas Wolf. 2022 · 2022
Closest in time.