2 citations · 4 across the 5 of their papers we have counts for
5 papers
Scaling Mobile Chaos Testing with AI-Driven Test Execution
Juan Marcano, Ashish Samant, Kai Song +9
Mobile applications in large-scale distributed systems are susceptible to backend service failures, yet traditional chaos engineering approaches cannot scale mobile testing due to…
LLMScan: Causal Scan for LLM Misbehavior Detection
Mengdi Zhang, Kai Kiat Goh, Peixin Zhang +3
Despite the success of Large Language Models (LLMs) across various fields, their potential to generate untruthful, biased and harmful responses poses significant risks, particularl…
Efficient and Universal Watermarking for LLM-Generated Code Detection
Boquan Li, Zirui Fu, Mengdi Zhang +3
Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and…
Adaptive Fairness Improvement Based on Causality Analysis
Mengdi Zhang, Jun Sun
Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accurac…
TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination
Mengdi Zhang, Jun Sun, Jingyi Wang +1
Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains such as face reco…