40 citations · 64 across the 6 of their papers we have counts for
6 papers
Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware
Yujia Chen, Mingyu Chen, Cuiyun Gao +3
Application Programming Interfaces (APIs) are crucial in modern software development. Large Language Models (LLMs) assist in automated code generation but often struggle with API h…
FastFixer: An Efficient and Effective Approach for Repairing Programming Assignments
Fang Liu, Zhenwei Liu, Qianhui Zhao +6
Providing personalized and timely feedback for student's programming assignments is useful for programming education. Automated program repair (APR) techniques have been used to fi…
Smaller but Better: Self-Paced Knowledge Distillation for Lightweight yet Effective LCMs
Yujia Chen, Yang Ye, Zhongqi Li +2
Large code models (LCMs) have remarkably advanced the field of code generation. Despite their impressive capabilities, they still face practical deployment issues, such as high inf…
Peer-aided Repairer: Empowering Large Language Models to Repair Advanced Student Assignments
Qianhui Zhao, Fang Liu, Li Zhang +6
Automated generation of feedback on programming assignments holds significant benefits for programming education, especially when it comes to advanced assignments. Automated Progra…
Beyond Functional Correctness: Exploring Hallucinations in LLM-Generated Code
Fang Liu, Yang Liu, Lin Shi +5
The rise of Large Language Models (LLMs) has significantly advanced various applications on software engineering tasks, particularly in code generation. Despite the promising perfo…
PanGu-Coder: Program Synthesis with Function-Level Language Modeling
Fenia Christopoulou, Gerasimos Lampouras, Milan Gritta +19
We present PanGu-Coder, a pretrained decoder-only language model adopting the PanGu-Alpha architecture for text-to-code generation, i.e. the synthesis of programming language solut…