1 citations · 1 across the 4 of their papers we have counts for
4 papers
Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation
Yiheng Shen, Wei Zheng, Xiao Wei +3
Large Language Models (LLMs) have shown remarkable potential in Verilog code generation, yet existing datasets contain con siderable noise and redundancy. Prior data selection meth…
Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead
Guang Yang, Wei Zheng, Xiang Chen +14
Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), attracting significant attention from bot…
The Cream Rises to the Top: Efficient Reranking Method for Verilog Code Generation
Guang Yang, Wei Zheng, Xiang Chen +3
LLMs face significant challenges in Verilog generation due to limited domain-specific knowledge. While sampling techniques improve pass@k metrics, hardware engineers need one trust…
CODE-DITING: A Reasoning-Based Metric for Functional Alignment in Code Evaluation
Guang Yang, Yu Zhou, Xiang Chen +5
Trustworthy evaluation methods for code snippets play a crucial role in neural code generation. Traditional methods, which either rely on reference solutions or require executable…