Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …

Large Language Models (LLMs), which bridge the gap between human language
understanding and complex problem-solving, achieve state-of-the-art performance on …

This paper introduces EmMark, a novel watermarking framework for protecting intellectual
property (IP) of embedded large language models deployed on resource-constrained edge …

The widely adopted and powerful generative large language models (LLMs) have raised
concerns about intellectual property rights violations and the spread of machine-generated …

Artificial Intelligence (AI) has steadily improved across a wide range of tasks. However, the
development and deployment of AI are almost entirely controlled by a few powerful …

Model merging is a promising lightweight model empowerment technique that does not rely
on expensive computing devices (eg, GPUs) or require the collection of specific training …

This survey paper investigates how personalized learning offered by Large Language
Models (LLMs) could transform educational experiences. We explore Knowledge Editing …

With the rapid advancement and extensive application of artificial intelligence technology,
large language models (LLMs) are extensively used to enhance production, creativity …

As the cost of training large language models (LLMs) rises, protecting their intellectual
property has become increasingly critical. Model merging, which integrates multiple expert …

As Large Language Models (LLMs) become increasingly sophisticated, they raise significant
security concerns, including the creation of fake news and academic misuse. Most detectors …