DOCE: Finding the Sweet Spot for Execution-Based Code Generation

Facebook fair’s WMT19 news translation task submission

Nathan Ng, Kyra Yee, Alexei Baevski, Myle Ott, Michael Auli, and Sergey Edunov. 2019 · 2019

Earlier work this paper cites.

Is MAP decoding all you need? the inadequacy of the mode in neural machine translation

Bryan Eikema and Wilker Aziz. 2020 · 2020

Earlier work this paper cites.

The curious case of neural text degeneration

Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020

Earlier work this paper cites.

Program synthesis with large language models

Original

Jacob Austin, Augustus Odena, Maxwell I. Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie J. Cai, Michael Terry, Quoc V. Le, and Charles Sutton. 2021 · 2021

Earlier work this paper cites.

Energy-based reranking: Improving neural machine translation using energy-based models

Sumanta Bhattacharyya, Amirmohammad Rooshenas, Subhajit Naskar, Simeng Sun, Mohit Iyyer, and Andrew McCallum. 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, Harri 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.

Training verifiers to solve math word problems

Original

Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021

Earlier work this paper cites.

Investigating softmax tempering for training neural machine translation models

Raj Dabre and Atsushi Fujita. 2021 · 2021

Earlier work this paper 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

Earlier work this paper cites.

Generate & rank: A multi-task framework for math word problems

Jianhao Shen, Yichun Yin, Lin Li, Lifeng Shang, Xin Jiang, Ming Zhang, and Qun Liu. 2021 · 2021

Earlier work this paper cites.

Sampling-based approximations to minimum bayes risk decoding for neural machine translation

Bryan Eikema and Wilker Aziz. 2022 · 2022

Earlier work this paper cites.

Quality-aware decoding for neural machine translation

Patrick Fernandes, António Farinhas, Ricardo Rei, José Guilherme Camargo de Souza, Perez Ogayo, Graham Neubig, and André F. T. Martins. 2022 · 2022

Earlier work this paper cites.

High quality rather than high model probability: Minimum bayes risk decoding with neural metrics

Markus Freitag, David Grangier, Qijun Tan, and Bowen Liang. 2022 · 2022

Earlier work this paper cites.

Fault-aware neural code rankers

Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andrés Codas, Mark Encarnación, Shuvendu K. Lahiri, Madanlal Musuvathi, and Jianfeng Gao. 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.

Natural language to code translation with execution

Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, and Sida I. Wang. 2022 · 2022

Earlier work this paper cites.

Codet: Code generation with generated tests

Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen. 2023 · 2023

Cited alongside, same era.

Codescore: Evaluating code generation by learning code execution

Original

Yihong Dong, Jiazheng Ding, Xue Jiang, Zhuo Li, Ge Li, and Zhi Jin. 2023 · 2023

Cited alongside, same era.

An empirical study of translation hypothesis ensembling with large language models

António Farinhas, José Guilherme Camargo de Souza, and André F. T. Martins. 2023 · 2023

Cited alongside, same era.

Incoder: A generative model for code infilling and synthesis

Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023

Cited alongside, same era.

Efficient memory management for large language model serving with pagedattention

Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023

Cited alongside, same era.

Coder reviewer reranking for code generation

Tianyi Zhang, Tao Yu, Tatsunori Hashimoto, Mike Lewis, Wen-Tau Yih, Daniel Fried, and Sida Wang. 2023 · 2023

Later among the works it cites.

Codebertscore: Evaluating code generation with pretrained models of code

Shuyan Zhou, Uri Alon, Sumit Agarwal, and Graham Neubig. 2023 · 2023

Later among the works it cites.

Teaching large language models to self-debug

Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2024 · 2024

Closest in time.

Deepseek-coder-v2: Breaking the barrier of closed-source models in code intelligence

Original

DeepSeek-AI, Qihao Zhu, Daya Guo, Zhihong Shao, Dejian Yang, Peiyi Wang, Runxin Xu, Y. Wu, Yukun Li, Huazuo Gao, Shirong Ma, Wangding Zeng, Xiao Bi, Zihui Gu, Hanwei Xu, Damai Dai, Kai Dong, Liyue Zhang, Yishi Piao, Zhibin Gou, Zhenda Xie, Zhewen Hao, Bingxuan Wang, Junxiao Song, Deli Chen, Xin Xie, Kang Guan, Yuxiang You, Aixin Liu, Qiushi Du, Wenjun Gao, Xuan Lu, Qinyu Chen, Yaohui Wang, Chengqi Deng, Jiashi Li, Chenggang Zhao, Chong Ruan, Fuli Luo, and Wenfeng Liang. 2024 · 2024

Closest in time.

Reranking laws for language generation: A communication-theoretic perspective