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Large Language Models (LLMs) have shown promising potentials in program generation and no-code automation.
A complexity measure
Thomas J McCabe · 1976
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Exploiting abstract syntax trees to locate software defects
Thomas Joshua Shippey · 2015
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Convolutional neural networks over control flow graphs for software defect prediction
Anh Viet Phan, Minh Le Nguyen, and Lam Thu Bui · 2017
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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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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, et al · 2020
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald · 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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The factual inconsistency problem in abstractive text summarization: A survey
Yichong Huang, Xiachong Feng, Xiaocheng Feng, and Bing Qin · 2021
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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, et al · 2021
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Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang · 2021
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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, et al · 2022
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Vibhor Agarwal, Yu Chen, and Nishanth Sastry · 2023
Cited alongside, same era.
Purple llama cyberseceval: A secure coding benchmark for language models
Manish Bhatt, Sahana Chennabasappa, Cyrus Nikolaidis, Shengye Wan, Ivan Evtimov, Dominik Gabi, Daniel Song, Faizan Ahmad, Cornelius Aschermann, Lorenzo Fontana, et al · 2023
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Large language models of code fail at completing code with potential bugs
Tuan Dinh, Jinman Zhao, Samson Tan, Renato Negrinho, Leonard Lausen, Sheng Zha, and George Karypis · 2023
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Ai in the gray: Exploring moderation policies in dialogic large language models vs. human answers in controversial topics
Vahid Ghafouri, Vibhor Agarwal, Yong Zhang, Nishanth Sastry, Jose Such, and Guillermo Suarez-Tangil · 2023
Cited alongside, same era.
Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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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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Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, and Xia Hu · 2023
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Detecting condition-related bugs with control flow graph neural network
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Xudong Liu, Chunming Hu, and Yang Liu · 2023
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Zhiqiang Hu, Yihuai Lan, Lei Wang, Wanyu Xu, Ee-Peng Lim, Roy Ka-Wei Lee, Lidong Bing, and Soujanya Poria · 2023
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Bias assessment and mitigation in llm-based code generation
Dong Huang, Qingwen Bu, Jie Zhang, Xiaofei Xie, Junjie Chen, and Heming Cui · 2023
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang · 2023
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Codeprompt: Improving source code-related classification with knowledge features through prompt learning
Yong Ma, Senlin Luo, Yu-Ming Shang, Yifei Zhang, and Zhengjun Li · 2023
Cited alongside, same era.
Gpt-4 technical report, 2023
OpenAI · 2023
Cited alongside, same era.
Llm is like a box of chocolates: the non-determinism of chatgpt in code generation
Shuyin Ouyang, Jie M Zhang, Mark Harman, and Meng Wang · 2023
Cited alongside, same era.
Ai-assisted coding: Experiments with gpt-4
Russell A Poldrack, Thomas Lu, and Gašper Beguš · 2023
Cited alongside, same era.
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
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Can chatgpt reproduce human-generated labels? a study of social computing tasks
Yiming Zhu, Peixian Zhang, Ehsan-Ul Haq, Pan Hui, and Gareth Tyson · 2023
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Software vulnerability and functionality assessment using llms
Rasmus Ingemann Tuffveson Jensen, Vali Tawosi, and Salwa Alamir · 2024
Closest in time.
Grace: Empowering llm-based software vulnerability detection with graph structure and in-context learning
Guilong Lu, Xiaolin Ju, Xiang Chen, Wenlong Pei, and Zhilong Cai · 2024
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Llama-3.1
MetaAI · 2024
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A new era in llm security: Exploring security concerns in real-world llm-based systems
Fangzhou Wu, Ning Zhang, Somesh Jha, Patrick McDaniel, and Chaowei Xiao · 2024
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Harnessing the power of llms in practice: A survey on chatgpt and beyond
Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Shaochen Zhong, Bing Yin, and Xia Hu · 2024
Closest in time.