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With the emergence of Large Language Models (LLMs), there has been a significant improvement in the programming capabilities of models, attracting growing attention from researchers.
CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2020 · 1909
Earlier work this paper cites.
Binary codes capable of correcting deletions, insertions, and reversals
Vladimir I Levenshtein. 1966 · 1966
Earlier work this paper cites.
Copilot–a hard real-time runtime monitor. In International Conference on Embedded Software . Springer, 213–222
Ananda Basu and et al. 2004 · 2004
Earlier work this paper cites.
Convolutional Neural Networks over Tree Structures for Programming Language Processing. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI)
Lili Mou, Rui Men, Ge Li, Yan Xu, Lu Zhang, and Zhi Jin. 2016 · 2016
Earlier work this paper cites.
RACE: Large-scale ReAding Comprehension Dataset From Examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017a · 2017
Earlier work this paper cites.
Code2Vec: Learning Distributed Representations of Code
Uri Alon, Meital Zilberstein, and Omer Levy. 2018 · 2018
Earlier work this paper cites.
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
Fine-tune BERT for extractive summarization. In Association for Computational Linguistics (ACL)
Yang Liu and Mirella Lapata. 2019 · 2019
Earlier work this paper cites.
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-label Text Classification. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
Kui Xu, Lei Wu, Zhiguang Wang, Fei Tian, Tong Liu, and Compling Pu. 2019 · 2019
Earlier work this paper cites.
DialoGPT: Large-scale generative pre-training for conversational response generation
Yizhe Zhang, Eric Sun, Zhiyuan Liu, Xin Li, Minlie Deng, and Yang Ji. 2019 · 2019
Earlier work this paper cites.
Meena: Open-domain conversational agent
Daniel K Adiwardana, Minh-Thang Lu, Robert Fergus, David Chen, et al · 2020
Earlier work this paper cites.
SAIL: A Situated Artificial Intelligence Language Model for Sentiment Analysis in Literary Texts. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations . 97–104
Deepayan Ghosal, Koustav Ghosh, Abir Chakraborty, Asif Ekbal, and Pushpak Bhattacharyya. 2020 · 2020
Earlier work this paper cites.
CodeBERT: A Pretrained Model for Programming and Natural Languages. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)
Linqing Huang, Pengcheng Yin, Wenpeng Guo, Xiaoya Zhang, Mo Yu, and Yinxuan Wang. 2020 · 2020
Earlier work this paper cites.
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Earlier work this paper cites.
Graph2Code: Generating Code Snippets from Graphical User Interfaces Using Graph Neural Networks. In Proceedings of the 28th ACM International Conference on Multimedia (MM)
Pengcheng Yao, Yiding Mao, Zijun Luo, Xiwei Li, and Ling Qin. 2020 · 2020
Earlier work this paper cites.
PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yaoqing Yang, Chris Tar, and Jason Baldridge. 2020 · 2020
Earlier work this paper cites.
Program Synthesis with Large Language Models
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.
ABCDM: An attention-based bidirectional CNN-RNN deep model for sentiment analysis
Mohammad Ehsan Basiri, Shahla Nemati, Moloud Abdar, Erik Cambria, and U Rajendra Acharya. 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, 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, Josh 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
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
Cited alongside, same era.
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, et al · 2021
Cited alongside, same era.
CodeXGLUE
Rui Huang, Xiaoya Zhang, and Furu Wei. Accessed: 2021 · 2021
Cited alongside, same era.
LLAMA: Leveraging Language Models for Zero-shot Semantic Parsing with Utterance Re-Writing
Michael Robb et al · 2021
Cited alongside, same era.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Yuhao Dan, Zhikai Lei, Yiyang Gu, Yong Li, Jianghao Yin, Jiaju Lin, Linhao Ye, Zhiyan Tiea, Yougen Zhou, Yilei Wang, Aimin Zhou, Ze Zhouand Qin Chen, Jie Zhou, Liang He, and Xipeng Qiu. 2023 · 2023
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Can ChatGPT pass high school exams on English language comprehension
Joost CF de Winter. 2023 · 2023
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How Ready are Pre-trained Abstractive Models and LLMs for Legal Case Judgement Summarization?
Aniket Deroy, Kripabandhu Ghosh, and Saptarshi Ghosh. 2023 · 2023
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Baby steps in evaluating the capacities of large language models
Michael C Frank. 2023 · 2023
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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment
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Yue Wang, Weishi Wang, Shafiq Joty, and Steven C. H. Hoi. 2021 · 2021
Cited alongside, same era.
Pangu-coder: Program synthesis with function-level language modeling
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta, Guchun Zhang, Yinpeng Guo, Zhongqi Li, Qi Zhang, Meng Xiao, Bo Shen, Lin Li, et al · 2022
Cited alongside, same era.
GLM: General Language Model Pretraining with Autoregressive Blank Infilling. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . 320–335
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Competition-level code generation with alphacode
Y Li, D Choi, J Chung, et al · 2022
Cited alongside, same era.
P-tuning: Prompt tuning can be comparable to fine-tuning across scales and tasks. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . 61–68
Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022 · 2022
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Cited alongside, same era.
Aidan Gilson, Conrad Safraneck, Thomas Huang, Vimig Socrates, Ling Chi, Richard Taylor, and David Chartash. 2023 · 2023
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Exploring the Responses of Large Language Models to Beginner Programmers’ Help Requests
Arto Hellas, Juho Leinonen, Sami Sarsa, Charles Koutcheme, Lilja Kujanpää, and Juha Sorva. 2023 · 2023
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C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models
Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, and Junxian He. 2023 · 2023
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Yunjie Ji, Yong Deng, Yan Gong, Yiping Peng, Qiang Niu, Lei Zhang, Baochang Ma, and Xiangang Li. 2023b · 2023
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Evaluating the use of large language model in identifying top research questions in gastroenterology
Adi Lahat, Eyal Shachar, Benjamin Avidan, Zina Shatz, Benjamin S Glicksberg, and Eyal Klang. 2023 · 2023
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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, Qian Liu, Evgenii Zheltonozhskii, Terry Yue Zhuo, Thomas Wang, Olivier Dehaene, Mishig Davaadorj, Joel Lamy-Poirier, João Monteiro, Oleh Shliazhko, Nicolas Gontier, Nicholas Meade, Armel Zebaze, Ming-Ho Yee, Logesh Kumar Umapathi, Jian Zhu, Benjamin Lipkin, Muhtasham Oblokulov, Zhiruo Wang, Rudra Murthy, Jason Stillerman, Siva Sankalp Patel, Dmitry Abulkhanov, Marco Zocca, Manan Dey, Zhihan Zhang, Nour Fahmy, Urvashi Bhattacharyya, Wenhao Yu, Swayam Singh, Sasha Luccioni, Paulo Villegas, Maxim Kunakov, Fedor Zhdanov, Manuel Romero, Tony Lee, Nadav Timor, Jennifer Ding, Claire Schlesinger, Hailey Schoelkopf, Jan Ebert, Tri Dao, Mayank Mishra, Alex Gu, Jennifer Robinson, Carolyn Jane Anderson, Brendan Dolan-Gavitt, Danish Contractor, Siva Reddy, Daniel Fried, Dzmitry Bahdanau, Yacine Jernite, Carlos Muñoz Ferrandis, Sean Hughes, Thomas Wolf, Arjun Guha, Leandro von Werra, and Harm de Vries. 2023 · 2023
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How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi, Weiwen Liu, Bo Chen, Xiangyang Li, Chenxu Zhu, Huifeng Guo, Yong Yu, Ruiming Tang, et al · 2023
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Jianghao Lin, Rong Shan, Chenxu Zhu, Kounianhua Du, Bo Chen, Shigang Quan, Ruiming Tang, Yong Yu, and Weinan Zhang. 2023b · 2023
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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
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At Which Training Stage Does Code Data Help LLMs Reasoning?
Yingwei Ma, Yue Liu, Yue Yu, Yuanliang Zhang, Yu Jiang, Changjian Wang, and Shanshan Li. 2023 · 2023
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Code llama: Open foundation models for code
B Roziere, J Gehring, F Gloeckle, et al · 2023
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MOSS: Training Conversational Language Models from Synthetic Data
Tianxiang Sun, Xiaotian Zhang, Zhengfu He, Peng Li, Qinyuan Cheng, Hang Yan, Xiangyang Liu, Yunfan Shao, Qiong Tang, Xingjian Zhao, Ke Chen, Yining Zheng, Zhejian Zhou, Ruixiao Li, Jun Zhan, Yunhua Zhou, Linyang Li, Xiaogui Yang, Lingling Wu, Zhangyue Yin, Xuanjing Huang, and Xipeng Qiu. 2023 · 2023
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InternLM: A Multilingual Language Model with Progressively Enhanced Capabilities
InternLM Team. 2023 · 2023
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Using an LLM to Help With Code Understanding
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers. 2024 · 2024
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DebugBench: Evaluating Debugging Capability of Large Language Models
Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Zhiyuan Liu, and Maosong Sun. 2024 · 2024
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Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, et al · 2024
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