5 papers
Semantic Consensus Decoding: Backdoor Defense for Verilog Code Generation
Guang Yang, Xing Hu, Xiang Chen +1
Large language models (LLMs) for Verilog code generation are increasingly adopted in hardware design, yet remain vulnerable to backdoor attacks where adversaries inject malicious t…
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…
Defending Code Language Models against Backdoor Attacks with Deceptive Cross-Entropy Loss
Guang Yang, Yu Zhou, Xiang Chen +4
Code Language Models (CLMs), particularly those leveraging deep learning, have achieved significant success in code intelligence domain. However, the issue of security, particularl…