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Found 6 papers, 1 papers with code
Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP).
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.
