Restoring Pruned Large Language Models via Lost Component Compensation

NeurIPSSpotlight2025

Authors
Zijian Feng, Hanzhang Zhou, Zixiao Zhu, Tianjiao Li, Chua Jia Jim Deryl, Mak Lee Onn, Gee Wah Ng, Kezhi Mao
Venue
NeurIPS 2025
Track
Spotlight

TL;DR

Pruning is a widely used technique to reduce the size and inference cost of large language models (LLMs), but it often causes performance degradation…

Opening excerpt from the authors’ abstract. source

Read the paper

Topics

large language model language model llm

Related NeurIPS papers

← All NeurIPS 2025 Spotlight papers · Browse the whole archive