CodeS: Natural Language to Code Repository via Multi-Layer Sketch

2 code implementations • 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.

Benchmarking

Improving Natural Language Capability of Code Large Language Model

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.

Code Generation Language Modelling +1

Can Programming Languages Boost Each Other via Instruction Tuning?

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.

PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

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.

Code Generation