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Found 4 papers, 1 papers with code
LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis
In this paper, we introduce the LLM factoscope, a novel Siamese network-based model that leverages the inner states of LLMs for factual detection.
Good-looking but Lacking Faithfulness: Understanding Local Explanation Methods through Trend-based Testing
While enjoying the great achievements brought by deep learning (DL), people are also worried about the decision made by DL models, since the high degree of non-linearity of DL models makes the decision extremely difficult to understand.
DeepObliviate: A Powerful Charm for Erasing Data Residual Memory in Deep Neural Networks
Compared to the method of retraining from scratch, our approach can achieve 99. 0%, 95. 0%, 91. 9%, 96. 7%, 74. 1% accuracy rates and 66. 7$\times$, 75. 0$\times$, 33. 3$\times$, 29. 4$\times$, 13. 7$\times$ speedups on the MNIST, SVHN, CIFAR-10, Purchase, and ImageNet datasets, respectively.
Towards Security Threats of Deep Learning Systems: A Survey
In order to unveil the security weaknesses and aid in the development of a robust deep learning system, we undertake an investigation on attacks towards deep learning, and analyze these attacks to conclude some findings in multiple views.
