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More specifically, it is analyzed why humans are able to understand textual adversarial examples, and a crucial point is observed that humans are adept at associating information from the context to comprehend a paragraph. Guided by this insight, the input text is segmented into subsentences, with the model simulating contextual comprehension by representing each subsentence as a particle within a mixture system, utilizing a density matrix to model inter-sentence interactions. A loss function integrating cross-entropy and orthogonality losses is employed to encourage the orthogonality of measurement states. Comprehensive experiments are conducted to validate the efficacy of proposed methodology, and the results underscore its superiority over baseline models even commercial applications based on large language models in terms of accuracy across diverse adversarial attack scenarios, showing the potential of proposed approach in enhancing the robustness of neural networks under adversarial attacks.<\/jats:p>","DOI":"10.1007\/s40747-024-01733-4","type":"journal-article","created":{"date-parts":[[2024,12,30]],"date-time":"2024-12-30T19:41:31Z","timestamp":1735587691000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Quantum theory-inspired inter-sentence semantic interaction model for textual adversarial defense"],"prefix":"10.1007","volume":"11","author":[{"given":"Jiacheng","family":"Huang","sequence":"first","affiliation":[]},{"given":"Long","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaoyin","family":"Yi","sequence":"additional","affiliation":[]},{"given":"Ning","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,30]]},"reference":[{"issue":"1","key":"1733_CR1","doi-asserted-by":"publisher","first-page":"2321555","DOI":"10.1080\/08839514.2024.2321555","volume":"38","author":"KS Kumar","year":"2024","unstructured":"Kumar KS, Radhamani AS, Kumar TA, Jalili A, Gheisari M, Malik Y, Chen H, Moshayedi AJ (2024) Sentiment analysis of short texts using SVMs and VSMs-based multiclass semantic classification. 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