llm-code-generation.md

Paper collection for LLM code generation

Introduction

Papers

1. DreamCoder: Bootstrapping Inductive Program Synthesis with Wake-Sleep Library Learning PLDI 2021

   Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sablé-Meyer, Lucas Morales, Luke Hewitt, Luc Cary, Armando Solar-Lezama, oshua B. Tenenbaum [pdf]

2. On-the-Fly Adaptation of Source Code Models using Meta-Learning NeurIPS 2020 CAP Workshop

   Disha Shrivastava, Hugo Larochelle, Daniel Tarlow [pdf], 2020.5.26

3. Competition-Level Code Generation with AlphaCode. Science 2022

   Yujia Li, David 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, Oriol Vinyals [pdf], 2022.2.8

4. An Exploratory Study on Code Attention in BERT. ICPC 2022

   Rishab Sharma, Fuxiang Chen, Fatemeh Fard, David Lo [pdf], 2022.4.5

5. CoCoSoDa: Effective Contrastive Learning for Code Search. ICSE 2023

   Ensheng Shi, Yanlin Wang, Wenchao Gu, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Hongbin Sun [pdf], 2022.4.7

6. Natural Language to Code Translation with Execution, EMNLP 2022

   Freda Shi, Daniel Fried, Marjan Ghazvininejad, Luke Zettlemoyer, Sida I. Wang [pdf], 2022.4.25

7. Fault-Aware Neural Code Rankers. NeurIPS 2022

   Jeevana Priya Inala, Chenglong Wang, Mei Yang, Andres Codas, Mark Encarnación, Shuvendu K Lahiri, Madanlal Musuvathi, Jianfeng Gao [pdf], 2022.6.4

8. Making Large Language Models Better Reasoners with Step-Aware Verifier. ACL 2023

   Yifei Li, Zeqi Lin, Shizhuo Zhang, Qiang Fu, Bei Chen, Jian-Guang Lou, Weizhu Chen [pdf], 2022.6.6

9. CodeS: Towards Code Model Generalization Under Distribution Shift. ICSE-NIER 2023

   Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon [pdf], 2022.6.11

10. NatGen: Generative pre-training by "Naturalizing" source code. ESEC/FSE 2022

    Saikat Chakraborty, Toufique Ahmed, Yangruibo Ding, Premkumar Devanbu, Baishakhi Ray [pdf], 2022.6.15

11. Repository-Level Prompt Generation for Large Language Models of Code. ICML 2023

    Disha Shrivastava, Hugo Larochelle, Daniel Tarlow [pdf], 2022.6.26

12. DocPrompting: Generating Code by Retrieving the Docs. ICLR 2023

    Shuyan Zhou, Uri Alon, Frank F. Xu, Zhiruo Wang, Zhengbao Jiang, Graham Neubig [pdf], 2022.7.13

13. CodeT: Code Generation with Generated Tests. ICLR 2023

    Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen [pdf], 2022.7.21

14. Neurosymbolic Repair for Low-Code Formula Languages. OOPSLA 2022

    Rohan Bavishi, Harshit Joshi, José Pablo Cambronero Sánchez, Anna Fariha, Sumit Gulwani, Vu Le, Ivan Radicek, Ashish Tiwari [pdf], 2022.7.24

15. Language Models Can Teach Themselves to Program Better. ICLR 2023

    Patrick Haluptzok, Matthew Bowers, Adam Tauman Kalai [pdf], 2022.7.29

16. CSSAM: Code Search via Attention Matching of Code Semantics and Structures. SANER 2023

    Yi Hu, Bo Cai, Yaoxiang Yu [pdf], 2022.8.8

17. Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation. ESEC/FSE 2022

    Sijie Shen, Xiang Zhu, Yihong Dong, Qizhi Guo, Yankun Zhen, Ge Li [pdf], 2022.8.22

18. Code4Struct: Code Generation for Few-Shot Event Structure Prediction. ACL 2023

    Xingyao Wang, Sha Li, Heng Ji [pdf], 2022.10.23

19. DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation. ICML 2023

    Yuhang Lai, Chengxi Li, Yiming Wang, Tianyi Zhang, Ruiqi Zhong, Luke Zettlemoyer, Scott Wen-tau Yih, Daniel Fried, Sida Wang, Tao Yu [pdf], 2022.11.18

20. Coder Reviewer Reranking for Code Generation. ICML 2023

    Tianyi Zhang, Tao Yu, Tatsunori B. Hashimoto, Mike Lewis, Wen-tau Yih, Daniel Fried, Sida I. Wang [pdf], 2022.11.29

21. Natural Language to Code Generation in Interactive Data Science Notebooks. ACL 2023

    Pengcheng Yin, Wen-Ding Li, Kefan Xiao, Abhishek Rao, Yeming Wen, Kensen Shi, Joshua Howland, Paige Bailey, Michele Catasta, Henryk Michalewski, Alex Polozov, Charles Sutton [pdf], 2022.12.19

22. Python Code Generation by Asking Clarification Questions. ACL 2023

    Haau-Sing Li, Mohsen Mesgar, André F. T. Martins, Iryna Gurevych [pdf], 2022.12.19

23. Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments. ACL 2023

    Yu Gu, Xiang Deng, Yu Su [pdf], 2022.12.19

24. Execution-Based Evaluation for Open-Domain Code Generation

    Zhiruo Wang, Shuyan Zhou, Daniel Fried, Graham Neubig [pdf], 2023.12.20

25. Large language models are versatile decomposers: Decompose evidence and questions for table-based reasoning. SIGIR 2023

    Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li [pdf], 2023.1.31

26. Learning Performance-Improving Code Edits Arxiv

    Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh [pdf], 2023.2.15

27. LEVER: Learning to Verify Language-to-Code Generation with Execution. ICML 2023

    Ansong Ni, Srini Iyer, Dragomir Radev, Ves Stoyanov, Wen-tau Yih, Sida I. Wang, Xi Victoria Lin [pdf], 2023.2.16

28. EvoPrompting: Language Models for Code-Level Neural Architecture Search. Arxiv

    Angelica Chen, David M. Dohan, David R. So [pdf], 2023.2.28

29. Planning with Large Language Models for Code Generation. ICLR 2023

    Shun Zhang, Zhenfang Chen, Yikang Shen, Mingyu Ding, Joshua B. Tenenbaum, Chuang Gan. [pdf], 2023.3.9

30. Self-planning Code Generation with Large Language Model. Arxiv

    Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, Ge Li [pdf], 2023.3.12

31. Reflexion: Language Agents with Verbal Reinforcement Learning. Arxiv

    Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, Shunyu Yao [pdf], 2023.3.20

32. Teaching Large Language Models to Self-Debug. Arxiv

    Xinyun Chen, Maxwell Lin, Nathanael Schärli, Denny Zhou [pdf], 2023.4.11

33. WizardLM: Empowering Large Language Models to Follow Complex Instructions. Arxiv

    Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang [pdf], 2023.4.24

34. Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation. ICML 2023

    Wenqing Zheng, S P Sharan, Ajay Kumar Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, Zhangyang Wang [pdf], 2023.4.28

35. From Words to Code: Harnessing Data for Program Synthesis from Natural Language. Arxiv

    Anirudh Khatry, Joyce Cahoon, Jordan Henkel, Shaleen Deep, Venkatesh Emani, Avrilia Floratou, Sumit Gulwani, Vu Le, Mohammad Raza, Sherry Shi, Mukul Singh, Ashish Tiwari [pdf], 2023.5.2

36. Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs. Arxiv

    Jinyang Li, Binyuan Hui, Ge Qu, Binhua Li, Jiaxi Yang, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C.C. Chang, Fei Huang, Reynold Cheng, Yongbin Li [pdf], 2023.5

37. Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation. Arxiv

    Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, Lingming Zhang [pdf], 2023.5.2

38. On Contrastive Learning of Semantic Similarity forCode to Code Search. Arxiv

    Anthony Saieva, Saikat Chakraborty, Gail Kaiser [pdf], 2023.5.5

39. Self-Edit: Fault-Aware Code Editor for Code Generation. ACL 2023

    Kechi Zhang, Zhuo Li, Jia Li, Ge Li, Zhi Jin [pdf], 2023.5.6

40. ToolCoder: Teach Code Generation Models to use API search tools Arxiv

    Kechi Zhang, Huangzhao Zhang, Ge Li, Jia Li, Zhuo Li, Zhi Jin [pdf], 2023.5.6

41. Code Execution with Pre-trained Language Models. ACL 2023 Findings

    Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan [pdf], 2023.5.8

42. StarCoder: may the source be with you! Arxiv

    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, Harm de Vries [pdf], 2023.5.9

43. SelfzCoT: a Self-Prompt Zero-shot CoT from Semantic-level to Code-level for a Better Utilization of LLMs. Arxiv

    IokTong Lei, ZhiDong Deng [pdf], 2023.5.19

44. Text-to-SQL Error Correction with Language Models of Code. ACL 2023

    Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun [pdf], 2023.5.22

45. ALGO: Synthesizing Algorithmic Programs with Generated Oracle Verifiers. Arxiv

    Kexun Zhang, Danqing Wang, Jingtao Xia, William Yang Wang, Lei Li [pdf], 2023.5.24

46. Tuning Models of Code with Compiler-Generated Reinforcement Learning Feedback. Arxiv

    Abhinav Jain, Chima Adiole, Swarat Chaudhuri, Thomas Reps, Chris Jermaine [pdf], 2023.5.25

47. SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL. Arxiv

    Ruoxi Sun, Sercan O. Arik, Hootan Nakhost, Hanjun Dai, Rajarishi Sinha, Pengcheng Yin, Tomas Pfister [pdf], 2023.5.26

48. Grammar Prompting for Domain-Specific Language Generation with Large Language Models. Arxiv

    Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao, Rif A. Saurous, Yoon Kim [pdf], 2023.5.30

49. SELFEVOLVE: A Code Evolution Framework via Large Language Models. Arxiv

    Shuyang Jiang, Yuhao Wang, Yu Wang [pdf], 2023.6.5

50. WizardCoder: Empowering Code Large Language Models with Evol-Instruct. Arxiv

    Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, Daxin Jiang [pdf], 2023.6.14

51. Demystifying GPT Self-Repair for Code Generation. Arxiv

    Theo X. Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, Armando Solar-Lezama [pdf], 2023.6.16

52. RepoFusion: Training Code Models to Understand Your Repository. Arxiv

    Disha Shrivastava, Denis Kocetkov, Harm de Vries, Dzmitry Bahdanau, Torsten Scholak [pdf], 2023.6.19

53. Guiding Language Models of Code with Global Context using Monitors. Arxiv

    Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, Sriram K. Rajamani [pdf], 2023.6.19

54. Textbooks Are All You Need. Arxiv

    Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li [pdf], 2023.6.20

55. Language models are weak learners. Arxiv

    Hariharan Manikandan, Yiding Jiang, J Zico Kolter [pdf], 2023.6.25

56. LongCoder: A Long-Range Pre-trained Language Model for Code Completion. Arxiv

    Daya Guo, Canwen Xu, Nan Duan, Jian Yin, Julian McAuley [pdf], 2023.6.26

57. InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback. Arxiv

    John Yang, Akshara Prabhakar, Karthik Narasimhan, Shunyu Yao [pdf], 2023.6.26

58. A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis Arxiv

    Izzeddin Gur, Hiroki Furuta, Austin Huang, Mustafa Safdari, Yutaka Matsuo, Douglas Eck, Aleksandra Faust [pdf], 2023.7.24

59. Predicting Code Coverage without Execution Arxiv

    Michele Tufano, Shubham Chandel, Anisha Agarwal, Neel Sundaresan, Colin Clement [pdf], 2023.7.25

60. ExeDec: Execution Decomposition for Compositional Generalization in Neural Program Synthesis Arxiv

    Kensen Shi, Joey Hong, Manzil Zaheer, Pengcheng Yin, Charles Sutton [pdf], 2023.7.26

61. Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models Arxiv

    Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, Tomas Pfister [pdf], 2023.8.1

62. Symmetry-Preserving Program Representations for Learning Code Semantics Arxiv

    Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana [pdf], 2023.8.7

63. OctoPack: Instruction Tuning Code Large Language Models

    Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, Shayne Longpre [pdf], 2023.8.14

64. Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification

    Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, Hongsheng Li [pdf], 2023.8.15

65. Does Asking Clarifying Questions Increases Confidence in Generated Code? On the Communication Skills of Large Language Models

    Jie JW Wu [pdf], 2023.8.25

66. BioCoder: A Benchmark for Bioinformatics Code Generation with Contextual Pragmatic Knowledge

    Xiangru Tang, Bill Qian, Rick Gao, Jiakang Chen, Xinyun Chen, Mark Gerstein [pdf], 2023.8.31

67. CodeApex: A Bilingual Programming Evaluation Benchmark for Large Language Models

    Lingyue Fu, Huacan Chai, Shuang Luo, Kounianhua Du, Weiming Zhang, Longteng Fan, Jiayi Lei, Renting Rui, Jianghao Lin, Yuchen Fang, Yifan Liu, Jingkuan Wang, Siyuan Qi, Kangning Zhang, Weinan Zhang, Yong Yu [pdf], 2023.9.5

68. Improving Code Generation by Dynamic Temperature Sampling

    Yuqi Zhu, Jia Li, Ge Li, YunFei Zhao, Jia Li, Zhi Jin, Hong Mei [pdf], 2023.9.6

69. RAP-Gen: Retrieval-Augmented Patch Generation with CodeT5 for Automatic Program Repair

    Weishi Wang, Yue Wang, Shafiq Joty, Steven C.H. Hoi [pdf], 2023.9.12

70. Safurai 001: New Qualitative Approach for Code LLM Evaluation

    Davide Cifarelli, Leonardo Boiardi, Alessandro Puppo [pdf], 2023.9.20

71. Program Repair with Minimal Edits Using CodeT5

    Atsushi Shirafuji, Md. Mostafizer Rahman, Md Faizul Ibne Amin, Yutaka Watanobe [pdf], 2023.9.26

72. L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models

    Ansong Ni, Pengcheng Yin, Yilun Zhao, Martin Riddell, Troy Feng, Rui Shen, Stephen Yin, Ye Liu, Semih Yavuz, Caiming Xiong, Shafiq Joty, Yingbo Zhou, Dragomir Radev, Arman Cohan [pdf], 2023.9.29

73. Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

    Baizhou Huang, Shuai Lu, Weizhu Chen, Xiaojun Wan, Nan Duan [pdf], 2023.9.29

74. L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation

    Samuel Holt, Max Ruiz Luyten, Mihaela van der Schaar [pdf], 2023.10.2

75. Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation

    Eric Zelikman, Eliana Lorch, Lester Mackey, Adam Tauman Kalai [pdf], 2023.10.3

76. $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis

    Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun, Hongxia Yang [pdf], 2023.10.4

77. Can Language Models Employ the Socratic Method? Experiments with Code Debugging

    Erfan Al-Hossami, Razvan Bunescu, Justin Smith, Ryan Teehan [pdf], 2023.10.4

78. MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning

    Ke Wang, Houxing Ren, Aojun Zhou, Zimu Lu, Sichun Luo, Weikang Shi, Renrui Zhang, Linqi Song, Mingjie Zhan, Hongsheng Li [pdf], 2023.10.5

79. The Program Testing Ability of Large Language Models for Code

    Weimin Xiong, Yiwen Guo, Hao Chen [pdf], 2023.10.9

80. SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan [pdf], 2023.10.10

81. CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules

    Hung Le, Hailin Chen, Amrita Saha, Akash Gokul, Doyen Sahoo, Shafiq Joty [pdf], 2023.10.13

82. Large Language Model-Aware In-Context Learning for Code Generation

    Jia Li, Ge Li, Chongyang Tao, Jia Li, Huangzhao Zhang, Fang Liu, Zhi Jin [pdf], 2023.10.15

83. CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion NeurIPS 2023

    Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Hantian Ding, Ming Tan, Nihal Jain, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, Bing Xiang [pdf], 2023.10.17

84. Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code Synthesis

    Philip John Gorinski, Matthieu Zimmer, Gerasimos Lampouras, Derrick Goh Xin Deik, Ignacio Iacobacci [pdf], 10.20

85. API-Assisted Code Generation for Question Answering on Varied Table Structures

    Yihan Cao, Shuyi Chen, Ryan Liu, Zhiruo Wang, Daniel Fried [pdf], 2023.10.23

86. Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation

    Jiexin Wang, Liuwen Cao, Xitong Luo, Zhiping Zhou, Jiayuan Xie, Adam Jatowt, Yi Cai [pdf], 2023.10.25

87. Symbolic Planning and Code Generation for Grounded Dialogue EMNLP 2023

    Justin T. Chiu, Wenting Zhao, Derek Chen, Saujas Vaduguru, Alexander M. Rush, Daniel Fried [pdf], 2023.10.26

88. Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation EMNLP 2023

    Hailin Chen, Amrita Saha, Steven Hoi, Shafiq Joty [pdf], 2023.10.28

89. LILO: LEARNING INTERPRETABLE LIBRARIES BY COMPRESSING AND DOCUMENTING CODE Arxiv

    Gabriel Grand, Lionel Wong, Matthew Bowers, Theo X. Olausson, Muxin Liu, Joshua B. Tenenbaum, Jacob Andreas [pdf], 2023.10.30

90. InstructCoder: Empowering Language Models for Code Editing Arxiv

    Qisheng Hu, Kaixin Li, Xu Zhao, Yuxi Xie, Tiedong Liu, Hui Chen, Qizhe Xie, Junxian He [pdf], 2023.10.31

91. Data Augmentation for Code Translation with Comparable Corpora and Multiple References, EMNLP 2023 Findings

    Yiqing Xie, Atharva Naik, Daniel Fried, Carolyn Rose [pdf], 2023.11.1

92. Safurai-Csharp: Harnessing Synthetic Data to improve language-specific Code LLM

    Davide Cifarelli, Leonardo Boiardi, Alessandro Puppo, Leon Jovanovic [pdf], 2023.11.6

93. Retrieval-Augmented Code Generation for Universal Information Extraction

    Yucan Guo, Zixuan Li, Xiaolong Jin, Yantao Liu, Yutao Zeng, Wenxuan Liu, Xiang Li, Pan Yang, Long Bai, Jiafeng Guo, Xueqi Cheng [pdf], 2023.11.6

94. Past as a Guide: Leveraging Retrospective Learning for Python Code Completion, Neurips 2023 Workshop

    Seunggyoon Shin, Seunggyu Chang, Sungjoon Choi [pdf], 2023.11.13

95. Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback

    Seungjun Moon, Yongho Song, Hyungjoo Chae, Dongjin Kang, Taeyoon Kwon, Kai Tzu-iunn Ong, Seung-won Hwang, Jinyoung Yeo [pdf], 2023.11.13

96. Explain-then-Translate: An Analysis on Improving Program Translation with Self-generated Explanations

    Zilu Tang, Mayank Agarwal, Alex Shypula, Bailin Wang, Derry Wijaya, Jie Chen, Yoon Kim, 2023.11.13

97. CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation

    Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Shuiguang Deng, Hari Sundaram [pdf], 2023.11.14

98. ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks

    Yuliang Liu, Xiangru Tang, Zefan Cai, Junjie Lu, Yichi Zhang, Yanjun Shao, Zexuan Deng, Helan Hu, Zengxian Yang, Kaikai An, Ruijun Huang, Shuzheng Si, Sheng Chen, Haozhe Zhao, Zhengliang Li, Liang Chen, Yiming Zong, Yan Wang, Tianyu Liu, Zhiwei Jiang, Baobao Chang, Yujia Qin, Wangchunshu Zhou, Yilun Zhao, Arman Cohan, Mark Gerstein [pdf], 2023.11.16

99. GenCodeSearchNet: A Benchmark Test Suite for Evaluating Generalization in Programming Language Understanding

    Andor Diera, Abdelhalim Dahou, Lukas Galke, Fabian Karl, Florian Sihler, Ansgar Scherp [pdf], 2023.11.16

100. Evaluating In-Context Learning of Libraries for Code Generation

     Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi [pdf], 2023.11.16

101. Function-constrained Program Synthesis, 2023 NeurIPS R0-Fomo Workshop

     Patrick Hajali, Ignas Budvytis [pdf], 2023.11.27

102. Applications of Large Language Models in Data Processing: Innovative Approaches to Segmenting and Renewing Information

     Yu-Chen Lin, Akhilesh Kumar, Wen-Liang Zhang, Norman Chang, Muhammad Zakir, Rucha Apte, Chao Wang, Jyh-Shing Roger Jang [pdf], 2023.11.27

103. Self-Infilling Code Generation

     Lin Zheng, Jianbo Yuan, Zhi Zhang, Hongxia Yang, Lingpeng Kong [pdf], 2023.11.29

104. Competition-Level Problems are Effective LLM Evaluators

     Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen [pdf], 2023.12.4

105. Magicoder: Source Code Is All You Need

     Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, Lingming Zhang [pdf], 2023.12.4

106. Chain of Code: Reasoning with a Language Model-Augmented Code Emulator

     Chengshu Li, Jacky Liang, Andy Zeng, Xinyun Chen, Karol Hausman, Dorsa Sadigh, Sergey Levine, Li Fei-Fei, Fei Xia, Brian Ichter [pdf], 2023.12.7