most citedEvaluating AIGC Detectors on Code Content

16 citations · 33 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR20248 cited

BadEdit: Backdooring large language models by model editing

Yanzhou Li, Tianlin Li, Kangjie Chen +5

Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applie…

cs.SE20239 cited

Exploring the Potential of ChatGPT in Automated Code Refinement: An Empirical Study

Qi Guo, Junming Cao, Xiaofei Xie +4

Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can signific…

cs.SE202316 cited

Evaluating AIGC Detectors on Code Content

Jian Wang, Shangqing Liu, Xiaofei Xie +1

Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces hig…

cs.SE20232 cited

ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning

Shangqing Liu, Bozhi Wu, Xiaofei Xie +2

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…

cs.CR20226 cited

Enhancing Security Patch Identification by Capturing Structures in Commits

Bozhi Wu, Shangqing Liu, Ruitao Feng +3

With the rapid increasing number of open source software (OSS), the majority of the software vulnerabilities in the open source components are fixed silently, which leads to the de…