6 citations · 14 across the 8 of their papers we have counts for
8 papers
Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation
Yu Yu, Zhihong Sun, Jia Li +8
Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies…
SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code
Qinglin Wang, Zhihong Sun, Ruyun Wang +4
Large Language Models (LLMs) can translate natural language requirements into code, yet empirical analyses of representative models reveal that semantic errors-programs that compil…
Ensembling Large Language Models for Code Vulnerability Detection: An Empirical Evaluation
Zhihong Sun, Jia Li, Yao Wan +7
Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models (LLMs) have shown promising capabiliti…
Sifting through the Chaff: On Utilizing Execution Feedback for Ranking the Generated Code Candidates
Zhihong Sun, Yao Wan, Jia Li +4
Large Language Models (LLMs), such as GPT-4, StarCoder, and CodeLlama, are transforming the way developers approach programming by automatically generating code based on given natu…
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs
Zhihong Sun, Chen Lyu, Bolun Li +4
Large Language Models (LLMs) have recently made significant advances in code generation through the 'Chain-of-Thought' prompting technique. This technique empowers the model to aut…
IRCoCo: Immediate Rewards-Guided Deep Reinforcement Learning for Code Completion
Bolun Li, Zhihong Sun, Tao Huang +5
Code completion aims to enhance programming productivity by predicting potential code based on the current programming context. Recently, pretrained language models (LMs) have beco…
Knowledge-Aware Code Generation with Large Language Models
Tao Huang, Zhihong Sun, Zhi Jin +2
Large Language Models (LLMs) perform well on basic programming problems. However, they encounter challenges when dealing with complex tasks involving the use of diverse algorithmic…
TACO: Topics in Algorithmic COde generation dataset
Rongao Li, Jie Fu, Bo-Wen Zhang +6
We introduce TACO, an open-source, large-scale code generation dataset, with a focus on the optics of algorithms, designed to provide a more challenging training dataset and evalua…