most citedArt and Science of Quantizing Large-Scale Models: A Comprehensive Overview

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

AgentRob: From Virtual Forum Agents to Hijacked Physical Robots

Wenrui Liu, Yaxuan Wang, Xun Zhang +13

Large Language Model (LLM)-powered autonomous agents have demonstrated significant capabilities in virtual environments, yet their integration with the physical world remains narro…

cs.CL2026

How Few-shot Demonstrations Affect Prompt-based Defenses Against LLM Jailbreak Attacks

Yanshu Wang, Shuaishuai Yang, Jingjing He +1

Large Language Models (LLMs) face increasing threats from jailbreak attacks that bypass safety alignment. While prompt-based defenses such as Role-Oriented Prompts (RoP) and Task-O…

cs.LG2024★ 1 cited

Art and Science of Quantizing Large-Scale Models: A Comprehensive Overview

Yanshu Wang, Tong Yang, Xiyan Liang +5

This paper provides a comprehensive overview of the principles, challenges, and methodologies associated with quantizing large-scale neural network models. As neural networks have…

cs.LG2024

QET: Enhancing Quantized LLM Parameters and KV cache Compression through Element Substitution and Residual Clustering

Yanshu Wang, Wang Li, Zhaoqian Yao +1

The matrix quantization entails representing matrix elements in a more space-efficient form to reduce storage usage, with dequantization restoring the original matrix for use. We f…

cs.LG2024

Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information

Yanshu Wang, Wenyang He, Tong Yang

Large Language Models (LLMs) have significantly advanced natural language processing tasks such as machine translation, text generation, and sentiment analysis. However, their larg…