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Exploring Continual Fine-Tuning for Enhancing Language Ability in Large Language Model
A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the model's performance on languages in which the model is already proficient (usually English).
SLIP: Securing LLMs IP Using Weights Decomposition
Current methods to protect models' IP on the edge have limitations in terms of practicality, loss in accuracy, or suitability to requirements.
On the Query Complexity of Training Data Reconstruction in Private Learning
For $(\epsilon, \delta)$ DP learners with training data drawn from any arbitrary compact metric space, we provide the \emph{first known lower bounds on the adversary's query complexity} as a function of the learner's privacy parameters.
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