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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

Showing 5160 of 114 papers

TitleStatusHype
TextDefense: Adversarial Text Detection based on Word Importance Entropy0
Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text AttacksCode0
RIATIG: Reliable and Imperceptible Adversarial Text-to-Image Generation With Natural PromptsCode1
A survey on text generation using generative adversarial networks0
Ignore Previous Prompt: Attack Techniques For Language ModelsCode2
TAPE: Assessing Few-shot Russian Language UnderstandingCode0
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks0
Adversarial Text Normalization0
SemAttack: Natural Textual Attacks via Different Semantic SpacesCode1
“That Is a Suspicious Reaction!”: Interpreting Logits Variation to Detect NLP Adversarial Attacks0
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