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 …

Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

As their size increases, Large Languages Models (LLMs) are natural candidates for network
pruning methods: approaches that drop a subset of network weights while striving to …

The popularity of LLaMA (Touvron et al., 2023a; b) and other recently emerged moderate-
sized large language models (LLMs) highlights the potential of building smaller yet powerful …

Extracting structured knowledge from scientific text remains a challenging task for machine
learning models. Here, we present a simple approach to joint named entity recognition and …

Large Language Models (LLMs) have revolutionized natural language processing tasks with
remarkable success. However, their formidable size and computational demands present …

Abstract Diffusion Probabilistic Models (DPMs) have recently demonstrated impressive
results on various generative tasks. Despite its promises, the learned representations of pre …

Adapter-style efficient transfer learning (ETL) has shown excellent performance in the tuning
of vision-language models (VLMs) under the low-data regime, where only a few additional …

Recently years have witnessed a rapid development of large language models (LLMs).
Despite the strong ability in many language-understanding tasks, the heavy computational …

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