5 citations · 25 across the 19 of their papers we have counts for
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cs.SE2023
Chain-of-Thought in Neural Code Generation: From and For Lightweight Language Models
Guang Yang, Yu Zhou, Xiang Chen +3
Large Language Models (LLMs) have demonstrated remarkable potential in code generation. The integration of Chain of Thought (CoT) reasoning can further boost their performance. How…
cs.SE2023★ 1 cited
Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation
Guang Yang, Yu Zhou, Xiangyu Zhang +3
Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particula…
cs.SE2023
A Syntax-Guided Multi-Task Learning Approach for Turducken-Style Code Generation
Guang Yang, Yu Zhou, Xiang Chen +4
Due to the development of pre-trained language models, automated code generation techniques have shown great promise in recent years. However, the generated code is difficult to me…