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Found 5 papers, 1 papers with code
Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP).
Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text
Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text.
NeuroView-RNN: It's About Time
A key interpretability issue with RNNs is that it is not clear how each hidden state per time step contributes to the decision-making process in a quantitative manner.
NeuroView: Explainable Deep Network Decision Making
Each member of the family is derived from a standard DN architecture by vector quantizing the unit output values and feeding them into a global linear classifier.
NFT-K: Non-Fungible Tangent Kernels
To further contribute interpretability with respect to classification and the layers, we develop a new network as a combination of multiple neural tangent kernels, one to model each layer of the deep neural network individually as opposed to past work which attempts to represent the entire network via a single neural tangent kernel.
