most citedEnhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs

5 citations · 5 across the 3 of their papers we have counts for

collaborators

3 papers

cs.SE2024

Measuring Code Efficiency Optimization Capabilities with ACEOB

Yue Pan, Xiuting Shao, Chen Lyu

As Moore's Law gains diminish, software performance and efficiency become increasingly vital. Optimizing code efficiency is challenging, even for professional programmers. However,…

cs.SE2024★ 5 cited

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…

cs.SE2024

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…