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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 81–90 of 114 papers

TitleStatusHype
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation—0
Adversarial Text-to-Image Synthesis: A Review—0
Persistent Anti-Muslim Bias in Large Language ModelsCode1
Generating Natural Language Attacks in a Hard Label Black Box SettingCode1
From Unsupervised Machine Translation To Adversarial Text Generation—0
Adversarial Text Generation via Sequence Contrast Discrimination—0
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation—0
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial ExamplesCode2
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the WildCode1
End-to-End Adversarial Text-to-SpeechCode1
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