Papers
2
Total Citations
9
H-Index
2
About
Mingfu Xue is a researcher focused on the security and robustness of natural language processing (NLP) systems, particularly intelligent question-and-answer (Q&A) robots. His work addresses critical vulnerabilities in AI-driven conversational agents, demonstrating how minor perturbations—such as typos or adversarial text modifications—can cause these systems to fail. In his most cited paper, "DPAEG: A Dependency Parse-Based Adversarial Examples Generation Method for Intelligent Q&A Robots" (2020, 7 citations), Xue introduced a novel method that leverages dependency parsing to craft adversarial examples, exposing weaknesses in NLP models and paving the way for more resilient architectures. His earlier study, "Robustness Analysis on Natural Language Processing Based AI Q&A Robots" (2019, 2 citations), laid foundational insights into the fragility of these systems under attack. By systematically analyzing and generating adversarial inputs, Xue’s contributions are vital for developing secure, trustworthy AI applications in real-world settings like customer service and virtual assistants. His work underscores the urgent need for adversarial robustness in NLP, making him a key voice in the intersection of AI security and natural language understanding.
Research Focus
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Top Papers
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