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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…
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…
Scrub It Out! Erasing Sensitive Memorization in Code Language Models via Machine Unlearning
Zhaoyang Chu, Yao Wan, Zhikun Zhang +7
While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a…
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…