McEval: Massively Multilingual Code Evaluation

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, et al. 2020 · 1901

Earlier work this paper cites.

Prompting is programming: A query language for large language models

Luca Beurer-Kellner, Marc Fischer, and Martin T. Vechev. 2023 · 1969

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.

Visualizing data using t-sne

Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008

Earlier work this paper cites.

Algorithms for hierarchical clustering: an overview

Fionn Murtagh and Pedro Contreras. 2012 · 2012

Earlier work this paper cites.

Quixbugs: a multi-lingual program repair benchmark set based on the quixey challenge

Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama. 2017 · 2017

Earlier work this paper cites.

Decoupled weight decay regularization

Original

Ilya Loshchilov and Frank Hutter. 2017 · 2017

Earlier work this paper cites.

Spoc: Search-based pseudocode to code

Sumith Kulal, Panupong Pasupat, Kartik Chandra, Mina Lee, Oded Padon, Alex Aiken, and Percy S Liang. 2019 · 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, and Ming Zhou. 2020 · 2020

Earlier work this paper cites.

Program synthesis with large language models

Original

Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. 2021 · 2021

Earlier work this paper cites.

Evaluating large language models trained on code

Original

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri 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, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua 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 · 2021

Earlier work this paper cites.

Prompt programming for large language models: Beyond the few-shot paradigm

Laria Reynolds and Kyle McDonell. 2021 · 2021

Earlier work this paper cites.

Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation

Original

Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021

Earlier work this paper cites.

Efficient training of language models to fill in the middle

Original

Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, and Mark Chen. 2022 · 2022

Earlier work this paper cites.

Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks

Original

Wenhu Chen, Xueguang Ma, Xinyi Wang, and William W. Cohen. 2022 · 2022

Earlier work this paper cites.

Incoder: A generative model for code infilling and synthesis

Original

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida I. Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen tau Yih, Luke Zettlemoyer, and Mike Lewis. 2022 · 2022

Earlier work this paper cites.

On distribution shift in learning-based bug detectors

Jingxuan He, Luca Beurer-Kellner, and Martin Vechev. 2022 · 2022

Earlier work this paper cites.

Competition-level code generation with alphacode

Original

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 J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals. 2022 · 2022

Earlier work this paper cites.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022

Earlier work this paper cites.

Bloom: A 176b-parameter open-access multilingual language model

Original

Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022

Earlier work this paper cites.

SantaCoder: Don’t reach for the stars!

Original

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 · 2023

Cited alongside, same era.

Multi-lingual evaluation of code generation models

Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, Sujan Kumar Gonugondla, Hantian Ding, Varun Kumar, Nathan Fulton, Arash Farahani, Siddhartha Jain, Robert Giaquinto, Haifeng Qian, Murali Krishna Ramanathan, and Ramesh Nallapati. 2023 · 2023

Cited alongside, same era.

Qwen technical report

Original

Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, Binyuan Hui, Luo Ji, Mei Li, Junyang Lin, Runji Lin, Dayiheng Liu, Gao Liu, Chengqiang Lu, Keming Lu, Jianxin Ma, Rui Men, Xingzhang Ren, Xuancheng Ren, Chuanqi Tan, Sinan Tan, Jianhong Tu, Peng Wang, Shijie Wang, Wei Wang, Shengguang Wu, Benfeng Xu, Jin Xu, An Yang, Hao Yang, Jian Yang, Shusheng Yang, Yang Yao, Bowen Yu, Hongyi Yuan, Zheng Yuan, Jianwei Zhang, Xingxuan Zhang, Yichang Zhang, Zhenru Zhang, Chang Zhou, Jingren Zhou, Xiaohuan Zhou, and Tianhang Zhu. 2023 · 2023

Cited alongside, same era.

Multipl-e: A scalable and polyglot approach to benchmarking neural code generation

Code llama: Open foundation models for code

Original

Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. 2023 · 2023

Later among the works it cites.

Execution-based evaluation for open-domain code generation

Original

Zhiruo Wang, Shuyan Zhou, Daniel Fried, and Graham Neubig. 2023 · 2023

Later among the works it cites.

Magicoder: Source code is all you need

Original

Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2023 · 2023

Later among the works it cites.

CodeTransOcean: A comprehensive multilingual benchmark for code translation

Weixiang Yan, Yuchen Tian, Yunzhe Li, Qian Chen, and Wen Wang. 2023 · 2023

Later among the works it cites.

Natural language to code generation in interactive data science notebooks