5 citations · 10 across the 5 of their papers we have counts for
5 papers
BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Mingxuan Xiao, Yan Xiao, Shunhui Ji +2
Fuzzing has shown great success in evaluating the robustness of intelligent natural language processing (NLP) software. As large language model (LLM)-based NLP software is widely d…
Convolutional neural network classification of cancer cytopathology images: taking breast cancer as an example
MingXuan Xiao, Yufeng Li, Xu Yan +2
Breast cancer is a relatively common cancer among gynecological cancers. Its diagnosis often relies on the pathology of cells in the lesion. The pathological diagnosis of breast ca…
Survival Prediction Across Diverse Cancer Types Using Neural Networks
Xu Yan, Weimin Wang, MingXuan Xiao +2
Gastric cancer and Colon adenocarcinoma represent widespread and challenging malignancies with high mortality rates and complex treatment landscapes. In response to the critical ne…
RITFIS: Robust input testing framework for LLMs-based intelligent software
Mingxuan Xiao, Yan Xiao, Hai Dong +2
The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testin…
LEAP: Efficient and Automated Test Method for NLP Software
Mingxuan Xiao, Yan Xiao, Hai Dong +2
The widespread adoption of DNNs in NLP software has highlighted the need for robustness. Researchers proposed various automatic testing techniques for adversarial test cases. Howev…