Fetching the paper…
Reading the bibliography…
This paper explores the limits of the current generation of large language models for program synthesis in general purpose programming languages.
Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi · 1905
Earlier work this paper cites.
CodeSearchNet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 1909
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 1910
Earlier work this paper cites.
Learning to fix build errors with Graph2Diff neural networks
Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, and Edward Aftandilian · 1911
Earlier work this paper cites.
The FORTRAN automatic coding system
J. W. Backus, R. J. Beeber, S. Best, R. Goldberg, L. M. Haibt, H. L. Herrick, R. A. Nelson, D. Sayre, P. B. Sheridan, H. Stern, I. Ziller, R. A. Hughes, and R. Nutt · 1957
Earlier work this paper cites.
PROW: A Step Toward Automatic Program Writing
R.J. Waldinger, R.C.T. Lee, and SRI International · 1969
Earlier work this paper cites.
Toward automatic program synthesis
Zohar Manna and Richard J Waldinger · 1971
Earlier work this paper cites.
Knowledge and reasoning in program synthesis
Zohar Manna and Richard Waldinger · 1975
Earlier work this paper cites.
Inferring LISP programs from examples
David E. Shaw, William R. Swartout, and C. Cordell Green · 1975
Earlier work this paper cites.
A methodology for LISP program construction from examples
Phillip D Summers · 1977
Earlier work this paper cites.
On the synthesis of a reactive module
Amir Pnueli and Roni Rosner · 1989
Earlier work this paper cites.
Learning to represent programs with property signatures
Augustus Odena and Charles Sutton · 2002
Earlier work this paper cites.
Code prediction by feeding trees to transformers
Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra · 2003
Earlier work this paper cites.
OptTyper: Probabilistic type inference by optimising logical and natural constraints
Irene Vlassi Pandi, Earl T Barr, Andrew D Gordon, and Charles Sutton · 2004
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
Earlier work this paper cites.
IntelliCode compose: Code generation using transformer
Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan · 2005
Earlier work this paper cites.
Dreamcoder: Growing generalizable, interpretable knowledge with wake-sleep bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell I. Nye, Mathias Sablé-Meyer, Luc Cary, Lucas Morales, Luke B. Hewitt, Armando Solar-Lezama, and Joshua B. Tenenbaum · 2006
Earlier work this paper cites.
Combinatorial sketching for finite programs
Armando Solar-Lezama, Liviu Tancau, Rastislav Bodík, Sanjit A. Seshia, and Vijay A. Saraswat · 2006
Earlier work this paper cites.
BUSTLE: bottom-up program-synthesis through learning-guided exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, and Charles Sutton · 2007
Earlier work this paper cites.
PyMT5: Multi-mode translation of natural language and python code with transformers
Colin B. Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan · 2010
Earlier work this paper cites.
Recurrent neural network based language model
Tomas Mikolov, Martin Karafiát, Lukas Burget, Jan Cernockỳ, and Sanjeev Khudanpur · 2010
Earlier work this paper cites.
Automating string processing in spreadsheets using input-output examples
Sumit Gulwani · 2011
Earlier work this paper cites.
Generating text with recurrent neural networks
Ilya Sutskever, James Martens, and Geoffrey E Hinton · 2011
Earlier work this paper cites.
Alan Turing’s Electronic Brain: The Struggle to Build the ACE, the World’s Fastest Computer
B.J. Copeland · 2012
Earlier work this paper cites.
On the “naturalness” of software
Abram Hindle, Earl Barr, Zhendong Su, Prem Devanbu, and Mark Gable · 2012
Earlier work this paper cites.
Sentencepiece: A simple and language independent subword tokenizer and detokenizer for neural text processing
Taku Kudo and John Richardson · 2012
Earlier work this paper cites.
GenProg: A generic method for automatic software repair
Claire Le Goues, Thanhvu Nguyen, Stephanie Forrest, and Westley Weimer · 2012
Earlier work this paper cites.
Syntax-guided synthesis
Rajeev Alur, Rastislav Bodík, Garvit Juniwal, Milo M. K. Martin, Mukund Raghothaman, Sanjit A. Seshia, Rishabh Singh, Armando Solar-Lezama, Emina Torlak, and Abhishek Udupa · 2013
Earlier work this paper cites.
Lexical statistical machine translation for language migration
Anh Tuan Nguyen, Tung Thanh Nguyen, and Tien N Nguyen · 2013
Earlier work this paper cites.
Growing solver-aided languages with rosette
Emina Torlak and Rastislav Bodik · 2013
Earlier work this paper cites.
Learning natural coding conventions
Miltiadis Allamanis, Earl T Barr, Christian Bird, and Charles Sutton · 2014
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Phrase-Based statistical translation of programming languages
Svetoslav Karaivanov, Veselin Raychev, and Martin Vechev · 2014
Earlier work this paper cites.
Structured generative models of natural source code
Chris J Maddison and Daniel Tarlow · 2014
Cited alongside, same era.
Code completion with statistical language models
Veselin Raychev, Martin Vechev, and Eran Yahav · 2014
Cited alongside, same era.
Towards a big data curated benchmark of inter-project code clones
Jeffrey Svajlenko, Judith F Islam, Iman Keivanloo, Chanchal K Roy, and Mohammad Mamun Mia · 2014
Cited alongside, same era.
Wojciech Zaremba and Ilya Sutskever · 2014
Cited alongside, same era.
Semi-supervised sequence learning
Andrew M Dai and Quoc V Le · 2015
Cited alongside, same era.
Predicting program properties from “big code”
Veselin Raychev, Martin Vechev, and Andreas Krause · 2015
Cited alongside, same era.
Hoppity: Learning graph transformations to detect and fix bugs in programs
Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang · 2019
Later among the works it cites.
Write, execute, assess: Program synthesis with a REPL
Kevin Ellis, Maxwell Nye, Yewen Pu, Felix Sosa, Josh Tenenbaum, and Armando Solar-Lezama · 2019
Later among the works it cites.
Global relational models of source code
Vincent J Hellendoorn, Charles Sutton, Rishabh Singh, Petros Maniatis, and David Bieber · 2019
Later among the works it cites.
SPoC: Search-based pseudocode to code
Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy Liang · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
Later among the works it cites.
Climbing towards NLU: On meaning, form, and understanding in the age of data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A Convolutional Attention Network for Extreme Summarization of Source Code
Miltiadis Allamanis, Hao Peng, and Charles Sutton · 2016
Cited alongside, same era.
Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio Gomez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, and Demis Hassabis · 2016
Cited alongside, same era.
Exploring the limits of language modeling
Rafal Józefowicz, Oriol Vinyals, Mike Schuster, Noam Shazeer, and Yonghui Wu · 2016
Cited alongside, same era.
Neural gpus learn algorithms
Lukasz Kaiser and Ilya Sutskever · 2016
Cited alongside, same era.
Neural random-access machines
Karol Kurach, Marcin Andrychowicz, and Ilya Sutskever · 2016
Cited alongside, same era.
Latent predictor networks for code generation
Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, and Andrew Senior · 2016
Cited alongside, same era.
Emily M. Bender and Alexander Koller · 2020
Later among the works it cites.
Learning to execute programs with instruction pointer attention graph neural networks
David Bieber, Charles Sutton, Hugo Larochelle, and Daniel Tarlow · 2020
Later among the works it cites.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, Alina Oprea, and Colin Raffel · 2020
Later among the works it 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, and Ming Zhou · 2020
Later among the works it cites.
Learning and evaluating contextual embedding of source code
Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi · 2020
Later among the works it cites.
Big code != big vocabulary: Open-Vocabulary models for source code
Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes · 2020
Later among the works it cites.
Where should I comment my code? A dataset and model for predicting locations that need comments
Annie Louis, Santanu Kumar Dash, Earl T Barr, Michael D Ernst, and Charles Sutton · 2020
Later among the works it cites.
TypeWriter: neural type prediction with search-based validation
Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra · 2020
Later among the works it cites.
Unsupervised translation of programming languages
Baptiste Roziere, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample · 2020
Later among the works it cites.
You autocomplete me: Poisoning vulnerabilities in neural code completion
Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov · 2020
Later among the works it cites.
LambdaNet: Probabilistic type inference using graph neural networks
Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dillig · 2020
Later among the works it cites.
Graph-based, self-supervised program repair from diagnostic feedback
Michihiro Yasunaga and Percy Liang · 2020
Later among the works it cites.
A large-scale benchmark for few-shot program induction and synthesis
Ferran Alet, Javier Lopez-Contreras, James Koppel, Maxwell Nye, Armando Solar-Lezama, Tomas Lozano-Perez, Leslie Kaelbling, and Joshua Tenenbaum · 2021
Closest in time.
A survey of machine learning on source code
Miltiadis Allamanis · 2021
Closest in time.
On the dangers of stochastic parrots: Can language models be too big?
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell · 2021
Closest in time.
Beyond the imitation game: Measuring and extrapolating the capabilities of language models
big-bench collaboration · 2021
Closest in time.
GPT-Neo: Large scale autoregressive language modeling with mesh-tensorflow, 2021
Sid Black, Leo Gao, Phil Wang, Connor Leahy, and Stella Biderman · 2021
Closest in time.
Evaluating large language models trained on code, July 2021
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harri 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 Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, Will Guss, Alex Nichol, Igor Babuschkin, Suchir Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Closest in time.
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
Closest in time.
Genline and genform: Two tools for interacting with generative language models in a code editor
Ellen Jiang, Edwin Toh, Alejandra Molina, Aaron Donsbach, Carrie Cai, and Michael Terry · 2021
Closest in time.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
Closest in time.
Implicit representations of meaning in neural language models
Belinda Z. Li, Maxwell Nye, and Jacob Andreas · 2021
Closest in time.
Prefix-Tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Closest in time.
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
Closest in time.
Studying the usage of Text-To-Text transfer transformer to support Code-Related tasks
Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, Rocco Oliveto, and Gabriele Bavota · 2021
Closest in time.
Deep Just-In-Time inconsistency detection between comments and source code
Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, and Raymond J Mooney · 2021
Closest in time.
Project 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, Vladmir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, and Ulrich Finkler · 2021
Closest in time.
Tal Schuster, Ashwin Kalyan, Oleksandr Polozov, and Adam Tauman Kalai · 2021
Closest in time.