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Assessing Adversarial Robustness of Large Language Models: An Empirical Study
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Large Language Models (LLMs) have revolutionized natural language processing, but their robustness against adversarial attacks remains a critical concern. We presents a novel white-box style attack approach that exposes vulnerabilities in leading open-source LLMs, including Llama, OPT, and T5. We assess the impact of model size, structure, and fine-tuning strategies on their resistance to adversarial perturbations. Our comprehensive evaluation across five diverse text classification tasks establishes a new benchmark for LLM robustness. The findings of this study have far-reaching implications for the reliable deployment of LLMs in real-world applications and contribute to the advancement of trustworthy AI systems.
Forward citations
Cited by 2 Pith papers
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This position paper classifies testing methods for LLM applications into three layers and proposes AICL, a structured protocol for testable agent communication; neither the framework nor the protocol is empirically validated.
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Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
A Jacobian-magnitude regularization called GBM improves empirical robustness of CNN/LSTM/S4 text classifiers to synonym-substitution attacks, but the claimed certified robustness is not delivered.
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