4 papers
DAQ: Delta-Aware Quantization for Post-Training LLM Weight Compression
Xiaoming Yu, Shize Tang, Guanghua Yu +4
We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge acquired during post-training. Standard quantization obje…
Stem: Rethinking Causal Information Flow in Sparse Attention
Lin Niu, Xin Luo, Linchuan Xie +4
The quadratic computational complexity of self-attention remains a fundamental bottleneck for scaling Large Language Models (LLMs) to long contexts, particularly during the pre-fil…
GTA: Supervised-Guided Reinforcement Learning for Text Classification with Large Language Models
Min Zeng, Jingfei Sun, Xueyou Luo +4
In natural language processing tasks, pure reinforcement learning (RL) fine-tuning methods often suffer from inefficient exploration and slow convergence; while supervised fine-tun…
Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy
Min Zeng, Caiquan Liu, Shiqi Zhang +3
In recent years, the use of large language models (LLMs) for text classification has attracted widespread attention. Despite this, the classification accuracy of LLMs has not yet u…