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Code generation aims to automatically generate code snippets that meet given natural language requirements and plays an important role in software development.
Answering the call for a standard reliability measure for coding data
Andrew F Hayes and Klaus Krippendorff. 2007 · 2007
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Latent predictor networks for code generation. In ACL
Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomáš Kočiskỳ, Andrew Senior, Fumin Wang, and Phil Blunsom. 2016 · 2016
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Tree-to-tree neural networks for program translation
Xinyun Chen, Chang Liu, and Dawn Song. 2018 · 2018
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Retrieval-based neural code generation. In EMNLP
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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Deep learning for source code modeling and generation: Models, applications, and challenges
Triet HM Le, Hao Chen, and Muhammad Ali Babar. 2020 · 2020
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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, et al · 2021
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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
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Exploring dynamic selection of branch expansion orders for code generation
Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie Zhou, Degen Huang, Qingqiang Wu, and Jinsong Su. 2021 · 2021
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OpenAI Code
OpenAI. 2021 · 2021
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Code completion by modeling flattened abstract syntax trees as graphs. In Proceedings of the AAAI conference on artificial intelligence , Vol. 35. 14015–14023
Yanlin Wang and Hui Li. 2021 · 2021
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Multi-lingual Evaluation of Code Generation Models. In The Eleventh International Conference on Learning Representations
Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, Mingyue Shang, et al · 2022
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FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. In Advances in Neural Information Processing Systems
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022
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Accelerating Code Search with Deep Hashing and Code Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 2534–2544
Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Michael Lyu. 2022 · 2022
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UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 7212–7225
Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022 · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. 2022 · 2022
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Guiding Language Models of Code with Global Context using Monitors
Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K Lahiri, and Sriram K Rajamani. 2023 · 2023
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Improving code generation by training with natural language feedback
Angelica Chen, Jérémy Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R Bowman, Kyunghyun Cho, and Ethan Perez. 2023 · 2023
Earlier work this paper cites.
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
Tri Dao. 2023 · 2023
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Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2023 · 2023
Cited alongside, same era.
bfloat16: The secret to high performance on Cloud TPUs
Google Cloud. 2020 · 2023
Cited alongside, same era.
Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency
Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, and Nan Duan. 2023 · 2023
Cited alongside, same era.
LLM-Assisted Code Cleaning For Training Accurate Code Generators
Naman Jain, Tianjun Zhang, Wei-Lin Chiang, Joseph E Gonzalez, Koushik Sen, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Efficient Memory Management for Large Language Model Serving with PagedAttention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al · 2023
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Magicoder: Source code is all you need. In International Conference on Machine Learning
Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2023 · 2023
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FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 14520–14535
Ruixuan Xiao, Yiwen Dong, Junbo Zhao, Runze Wu, Minmin Lin, Gang Chen, and Haobo Wang. 2023 · 2023
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Exploring continual learning for code generation models
Prateek Yadav, Qing Sun, Hantian Ding, Xiaopeng Li, Dejiao Zhang, Ming Tan, Xiaofei Ma, Parminder Bhatia, Ramesh Nallapati, Murali Krishna Ramanathan, et al · 2023
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Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023
Cited alongside, same era.
Fast inference from transformers via speculative decoding. In International Conference on Machine Learning . PMLR, 19274–19286
Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023 · 2023
Cited alongside, same era.
Structured Chain-of-Thought Prompting for Code Generation
Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023b · 2023
Cited alongside, same era.
Large Language Model-Aware In-Context Learning for Code Generation
Jia Li, Ge Li, Chongyang Tao, Huangzhao Zhang, Fang Liu, and Zhi Jin. 2023c · 2023
Cited alongside, same era.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
Cited alongside, same era.
Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation
Xin-Ye Li, Jiang-Tian Xue, Zheng Xie, and Ming Li. 2023d · 2023
Cited alongside, same era.
Refining ChatGPT-generated code: Characterizing and mitigating code quality issues
Yue Liu, Thanh Le-Cong, Ratnadira Widyasari, Chakkrit Tantithamthavorn, Li Li, Xuan-Bach D Le, and David Lo. 2023 · 2023
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. 2023 · 2023
Cited alongside, same era.
Daoguang Zan, Bei Chen, Yongshun Gong, Junzhi Cao, Fengji Zhang, Bingchao Wu, Bei Guan, Yilong Yin, and Yongji Wang. 2023a · 2023
Later among the works it cites.
RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing . 2471–2484
Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. 2023c · 2023
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Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding
Jun Zhang, Jue Wang, Huan Li, Lidan Shou, Ke Chen, Gang Chen, and Sharad Mehrotra. 2023e · 2023
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Planning with large language models for code generation
Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B Tenenbaum, and Chuang Gan. 2023b · 2023
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A Survey on Language Models for Code
Ziyin Zhang, Chaoyu Chen, Bingchang Liu, Cong Liao, Zi Gong, Hang Yu, Jianguo Li, and Rui Wang. 2023a · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends
Zibin Zheng, Kaiwen Ning, Yanlin Wang, Jingwen Zhang, Dewu Zheng, Mingxi Ye, and Jiachi Chen. 2023 · 2023
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Improving Code Generation by Dynamic Temperature Sampling
Yuqi Zhu, Jia Allen Li, Ge Li, YunFei Zhao, Jia Li, Zhi Jin, and Hong Mei. 2023 · 2023
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CodeFast
Deep Software Analytics. 2024 · 2024
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Codeplan: Repository-level coding using llms and planning
Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, and Shashank Shet. 2024 · 2024
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Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings
Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. 2024 · 2024
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AceCoder: An Effective Prompting Technique Specialized in Code Generation
Jia Li, Yunfei Zhao, Yongmin Li, Ge Li, and Zhi Jin. 2024 · 2024
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When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE, 906–917
Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, and Li Li. 2024a · 2024
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AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation. In 33st ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2024
Zhensu Sun, Xiaoning Du, Zhou Yang, Li Li, and David Lo. 2024b · 2024
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RLCoder: Reinforcement Learning for Repository-Level Code Completion
Yanlin Wang, Yanli Wang, Daya Guo, Jiachi Chen, Ruikai Zhang, Yuchi Ma, and Zibin Zheng. 2024 · 2024
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CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pretrained Models. In 2024 IEEE/ACM 46th International Conference on Software Engineering (ICSE) . IEEE, 428–439
Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie. 2024 · 2024
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Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond
Dewu Zheng, Yanlin Wang, Ensheng Shi, Ruikai Zhang, Yuchi Ma, Hongyu Zhang, and Zibin Zheng. 2024 · 2024
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