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Code generation has attracted increasing attention with the rise of Large Language Models (LLMs).
Program synthesis
Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, et al. 2017 · 2017
Earlier work this paper cites.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, and Charles Sutton. 2021 · 2021
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 · 2021
Earlier work this paper cites.
Multipl-e: A scalable and extensible approach to benchmarking neural code generation
Federico Cassano, John Gouwar, Daniel Nguyen, Sydney Nguyen, Luna Phipps-Costin, Donald Pinckney, Ming-Ho Yee, Yangtian Zi, Carolyn Jane Anderson, Molly Q Feldman, et al. 2022 · 2022
Earlier work this paper cites.
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 · 2022
Earlier work this paper cites.
Code alpaca: An instruction-following llama model for code generation
Sahil Chaudhary. 2023 · 2023
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
Earlier work this paper cites.
Large language models can self-improve
Jiaxin Huang, Shixiang Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han. 2023 · 2023
Earlier work this paper cites.
Is your code generated by chatGPT really correct? rigorous evaluation of large language models for code generation
Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and LINGMING ZHANG. 2023 · 2023
Earlier work this paper cites.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. 2023 · 2023
Earlier work this paper cites.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
Earlier work this paper cites.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, et al. 2023 · 2023
Earlier work this paper cites.
Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
Earlier work this paper cites.
Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku
Anthropic. 2024 · 2024
Cited alongside, same era.
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2024 · 2024
Cited alongside, same era.
Deepseek-coder: When the large language model meets programming–the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Yu Wu, YK Li, et al. 2024 · 2024
Cited alongside, same era.
Teaching language models to self-improve by learning from language feedback
Chi Hu, Yimin Hu, Hang Cao, Tong Xiao, and JingBo Zhu. 2024 · 2024
Cited alongside, same era.
Qwen2.5-coder technical report
Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Dayiheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Kai Dang, et al. 2024 · 2024
Cited alongside, same era.
Self-rewarding language models
Weizhe Yuan, Richard Yuanzhe Pang, Kyunghyun Cho, Xian Li, Sainbayar Sukhbaatar, Jing Xu, and Jason E Weston. 2024 · 2024
Later among the works it cites.
OpenCodeInterpreter: Integrating code generation with execution and refinement
Tianyu Zheng, Ge Zhang, Tianhao Shen, Xueling Liu, Bill Yuchen Lin, Jie Fu, Wenhu Chen, and Xiang Yue. 2024a · 2024
Later among the works it cites.
Debug like a human: A large language model debugger via verifying runtime execution step by step
Li Zhong, Zilong Wang, and Jingbo Shang. 2024 · 2024
Later among the works it cites.
Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, et al. 2024 · 2024
Later among the works it cites.
Revisit self-debugging with self-generated tests for code generation
Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, and Zhi Jin. 2025 · 2025
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Livecodebench: Holistic and contamination free evaluation of large language models for code
Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Armando Solar-Lezama, Koushik Sen, and Ion Stoica. 2024 · 2024
Cited alongside, same era.
From generation to judgment: Opportunities and challenges of llm-as-a-judge
Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, and Huan Liu. 2024 · 2024
Cited alongside, same era.
Wizardcoder: Empowering code large language models with evol-instruct
Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2024 · 2024
Cited alongside, same era.
Codelutra: Boosting llm code generation via preference-guided refinement
Leitian Tao, Xiang Chen, Tong Yu, Tung Mai, Ryan Rossi, Yixuan Li, and Saayan Mitra. 2024 · 2024
Cited alongside, same era.
How do your code LLMs perform? empowering code instruction tuning with really good data
Yejie Wang, Keqing He, Dayuan Fu, Zhuoma GongQue, Heyang Xu, Yanxu Chen, Zhexu Wang, Yujia Fu, Guanting Dong, Muxi Diao, Jingang Wang, Mengdi Zhang, Xunliang Cai, and Weiran Xu. 2024b · 2024
Cited alongside, same era.
Inversecoder: Self-improving instruction-tuned code llms with inverse-instruct
Yutong Wu, Di Huang, Wenxuan Shi, Wei Wang, Lingzhe Gao, Shihao Liu, Ziyuan Nan, Kaizhao Yuan, Rui Zhang, Xishan Zhang, Zidong Du, Qi Guo, Yewen Pu, Dawei Yin, Xing Hu, and Yunji Chen. 2024 · 2024
Cited alongside, same era.
WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Qingwei Lin, and Daxin Jiang. 2024 · 2024
Cited alongside, same era.
Closest in time.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
DeepSeek-AI. 2025 · 2025
Closest in time.
Self-boosting large language models with synthetic preference data
Qingxiu Dong, Li Dong, Xingxing Zhang, Zhifang Sui, and Furu Wei. 2025 · 2025
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Logicpro: Improving complex logical reasoning via program-guided learning
Jin Jiang, Yuchen Yan, Yang Liu, Yonggang Jin, Shuai Peng, Mengdi Zhang, Xunliang Cai, Yixin Cao, Liangcai Gao, and Zhi Tang. 2025 · 2025
Closest in time.
Spread preference annotation: Direct preference judgment for efficient LLM alignment
Dongyoung Kim, Jaehyung Kim, Kimin Lee, and Jinwoo Shin. 2025 · 2025
Closest in time.
Codeelo: Benchmarking competition-level code generation of llms with human-comparable elo ratings
Shanghaoran Quan, Jiaxi Yang, Bowen Yu, Bo Zheng, Dayiheng Liu, An Yang, Xuancheng Ren, Bofei Gao, Yibo Miao, Yunlong Feng, et al. 2025 · 2025
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Critique fine-tuning: Learning to critique is more effective than learning to imitate
Yubo Wang, Xiang Yue, and Wenhu Chen. 2025 · 2025
Closest in time.
S3cmath: Spontaneous step-level self-correction makes large language models better mathematical reasoners
Yuchen Yan, Jin Jiang, Yang Liu, Yixin Cao, Xin Xu, Mengdi Zhang, Xunliang Cai, and Jian Shao. 2025 · 2025
Closest in time.