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Adversarial Text

Adversarial Text refers to a specialised text sequence that is designed specifically to influence the prediction of a language model. Generally, Adversarial Text attack are carried out on Large Language Models (LLMs). Research on understanding different adversarial approaches can help us build effective defense mechanisms to detect malicious text input and build robust language models.

Papers

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Showing 31–40 of 114 papers

TitleStatusHype
From Unsupervised Machine Translation To Adversarial Text Generation—0
Adversarial Text Normalization—0
Adversarial Text Generation Without Reinforcement Learning—0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
Fooling OCR Systems with Adversarial Text Images—0
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
A survey on text generation using generative adversarial networks—0
Adversarial Text Generation with Dynamic Contextual Perturbation—0
Adversarial Text Generation via Sequence Contrast Discrimination—0
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