Papers with auxiliary embedding
RAFT: Realistic Attacks to Fool Text Detectors (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have exhibited remarkable fluency across tasks, but their unethical applications are unclear. |
| Approach: | They propose a grammar error-free black-box attack that exploits LLM embeddings at the word-level while preserving original text quality. |
| Outcome: | The proposed attack compromises all detectors across domains and is transferable across source models. |