1 code implementation • 25 Mar 2024 • Daoguang Zan, Ailun Yu, Wei Liu, Dong Chen, Bo Shen, Wei Li, Yafen Yao, Yongshun Gong, Xiaolin Chen, Bei guan, Zhiguang Yang, Yongji Wang, Qianxiang Wang, Lizhen Cui
For feedback-based evaluation, we develop a VSCode plugin for CodeS and engage 30 participants in conducting empirical studies.
1 code implementation • 25 Jan 2024 • Wei Li, Daoguang Zan, Bei guan, Ailun Yu, Xiaolin Chen, Yongji Wang
Code large language models (Code LLMs) have demonstrated remarkable performance in code generation.
no code implementations • 17 Jan 2024 • Xiaolin Chen, Daoguang Zan, Wei Li, Bei guan, Yongji Wang
Specifically, the malicious participant initially employs semi-supervised learning to train a surrogate target model.
1 code implementation • 31 Aug 2023 • Daoguang Zan, Ailun Yu, Bo Shen, Jiaxin Zhang, Taihong Chen, Bing Geng, Bei Chen, Jichuan Ji, Yafen Yao, Yongji Wang, Qianxiang Wang
Results demonstrate that programming languages can significantly improve each other.
no code implementations • 27 Jul 2023 • Bo Shen, Jiaxin Zhang, Taihong Chen, Daoguang Zan, Bing Geng, An Fu, Muhan Zeng, Ailun Yu, Jichuan Ji, Jingyang Zhao, Yuenan Guo, Qianxiang Wang
In this paper, we propose a novel RRTF (Rank Responses to align Test&Teacher Feedback) framework, which can effectively and efficiently boost pre-trained large language models for code generation.
Ranked #28 on Code Generation on HumanEval
no code implementations • 15 Apr 2023 • Bingchao Wu, Yangyuxuan Kang, Daoguang Zan, Bei guan, Yongji Wang
Specifically, for avoiding the exponential expansion of neighbors, we propose a hierarchical message aggregation mechanism to interact separately with low-order neighbors and meta-path-constrained high-order neighbors.
1 code implementation • 22 Mar 2023 • Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen
The task of repository-level code completion is to continue writing the unfinished code based on a broader context of the repository.
no code implementations • 19 Dec 2022 • Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei guan, Yongji Wang, Jian-Guang Lou
The task of generating code from a natural language description, or NL2Code, is considered a pressing and significant challenge in code intelligence.
1 code implementation • 31 Oct 2022 • Daoguang Zan, Bei Chen, Zeqi Lin, Bei guan, Yongji Wang, Jian-Guang Lou
In this paper, we investigate how to equip pre-trained language models with the ability of code generation for private libraries.
1 code implementation • 21 Jul 2022 • Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, Weizhu Chen
A natural way to evaluate the quality and correctness of a code solution is to run it against a set of test cases, but the manual creation of such test cases is often costly and time-consuming.
Ranked #1 on Code Generation on APPS (Introductory Pass@1 metric)
1 code implementation • 14 Jun 2022 • Daoguang Zan, Bei Chen, Dejian Yang, Zeqi Lin, Minsu Kim, Bei guan, Yongji Wang, Weizhu Chen, Jian-Guang Lou
Usually, expensive text-code paired data is essential for training a code generation model.
Ranked #117 on Code Generation on HumanEval