Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Modern deep neural networks, particularly recent large language models, come with
massive model sizes that require significant computational and storage resources. To …

Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …

Neural Networks can be efficiently compressed through pruning, significantly reducing
storage and computational demands while maintaining predictive performance. Simple yet …

Pruning large language models (LLMs) is a challenging task due to their enormous size.
The primary difficulty is fine-tuning the model after pruning, which is needed to recover the …

Despite the remarkable capabilities, Large Language Models (LLMs) face deployment
challenges due to their extensive size. Pruning methods drop a subset of weights to …

Test-time interventions for language models can enhance factual accuracy, mitigate harmful
outputs, and improve model efficiency without costly retraining. But despite a flood of new …

Large Language Models (LLMs) are distinguished by their massive parameter counts, which
typically result in significant redundancy. This work introduces MaskLLM, a learnable …

Powerful foundation models, including large language models (LLMs), with Transformer
architectures have ushered in a new era of Generative AI across various industries. Industry …

Compared to the moderate size of neural network models, structural weight pruning on the
Large-Language Models (LLMs) imposes a novel challenge on the efficiency of the pruning …