Papers with normal inference
On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing adversarial detection methods require access to training data, which brings noteworthy concerns regarding privacy leakage and generalizability. |
| Approach: | They propose a data-agnostic adversarial detection framework which induces different responses between normal and adversarials to UAPs. |
| Outcome: | The proposed framework achieves competitive detection performance on various text classification tasks, and maintains equivalent time consumption to normal inference. |
When Benchmarks Leak: Inference-Time Decontamination for LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | a large number of large language models (LLMs) are being evaluated for their performance, but their reliability is threatened by test set contamination. |
| Approach: | They propose a framework that decontaminates large language models by applying small perturbations to the input embedding space. |
| Outcome: | The proposed framework achieves strong decontamination effectiveness while incurring minimal degradation in benign utility. |