1 code implementation • 6 May 2023 • Kechi Zhang, Zhuo Li, Jia Li, Ge Li, Zhi Jin
Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LLMs to improve the code quality on the competitive programming task.
1 code implementation • 14 Mar 2023 • Kechi Zhang, Zhuo Li, Zhi Jin, Ge Li
Furthermore, we propose the Hierarchy Transformer (HiT), a simple but effective sequence model to incorporate the complete hierarchical embeddings of source code into a Transformer model.
1 code implementation • 31 Oct 2022 • Jia Li, Ge Li, Zhuo Li, Zhi Jin, Xing Hu, Kechi Zhang, Zhiyi Fu
Pre-trained models are first pre-trained with pre-training tasks and fine-tuned with the code editing task.
1 code implementation • 18 Aug 2022 • Wenhan Wang, Kechi Zhang, Ge Li, Shangqing Liu, Anran Li, Zhi Jin, Yang Liu
Learning vector representations for programs is a critical step in applying deep learning techniques for program understanding tasks.
no code implementations • 18 Jul 2022 • Kechi Zhang, Ge Li, Zhi Jin
In the field of source code processing, the transformer-based representation models have shown great powerfulness and have achieved state-of-the-art (SOTA) performance in many tasks.
no code implementations • 8 Dec 2020 • Kechi Zhang, Wenhan Wang, Huangzhao Zhang, Ge Li, Zhi Jin
To address the information of node and edge types, we bring the idea of heterogeneous graphs to learning on source code and present a new formula of building heterogeneous program graphs from ASTs with additional type information for nodes and edges.