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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 51–60 of 114 papers

TitleStatusHype
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
A Grey-box Text Attack Framework using Explainable AI—0
A survey on text generation using generative adversarial networks—0
Autonomous LLM-Enhanced Adversarial Attack for Text-to-Motion—0
PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack for Toxicity Maximization—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
Data-Driven Mitigation of Adversarial Text Perturbation—0
Detecting Adversarial Text Attacks via SHapley Additive exPlanations—0
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