Backdoor Learning in Language Models and Vision-Language Models
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Computer Science > Computation and Language
Title:Backdoor Learning in Language Models and Vision-Language Models
Abstract:Recent advances in deep learning have significantly enhanced the capabilities of Natural Language Processing (NLP) and Vision-Language Models (VLMs). However, these advancements come with increased vulnerabilities, notably through backdoor attacks that pose severe security threats. This thesis addresses two critical dimensions of Trustworthy AI and Efficient Multimodal Representation Learning: (1) security through analyzing, detecting, and designing backdoor attacks in NLP and VLMs, and (2) efficiency through advanced multimodal representation methods tailored for clinical and medical imaging applications.
| Comments: | Ph.D. dissertation |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.18095 [cs.CL] |
| (or arXiv:2608.18095v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.18095
arXiv-issued DOI via DataCite
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