The machine learning community has witnessed impressive advancements since large
language models (LLMs) first appeared. Yet, their massive memory consumption has …

Large Language Models (LLMs) have demonstrated remarkable capabilities across various
fields, from natural language understanding to text generation. Compared to non-generative …

In this paper, we present DSA, the first automated framework for discovering sparsity
allocation schemes for layer-wise pruning in Large Language Models (LLMs). LLMs have …

Large language models (LLMs) are increasingly being adopted in a wide range of real-
world applications. Despite their impressive performance, recent studies have shown that …

With the remarkable advancements of large language models (LLMs), LLM-based agents
have become a research hotspot in human-computer interaction. However, there is a …

Large language models have shown an impressive societal impact owing to their excellent
understanding and logical reasoning skills. However, such strong ability relies on a huge …

Retrieval-Augmented Generation (RAG) offers an effective solution to the issues faced by
Large Language Models (LLMs) in hallucination generation and knowledge obsolescence …

The massive parameters and computational demands hinder the widespread application of
Large Language Models (LLMs). Network pruning provides a practical solution to this …

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

The rapid growth of Large Language Models (LLMs) usage has highlighted the importance
of gradient-free in-context learning (ICL). However, interpreting their inner workings remains …