Do Compressed LLMs Forget Knowledge? An Experimental Study with
Practical Implications

Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications

2 October 2023

Duc N. M. Hoang

Minsik Cho

Mohammad Rastegari

Papers citing "Do Compressed LLMs Forget Knowledge? An Experimental Study with Practical Implications"

5 / 5 papers shown

Title
Introspective Growth: Automatically Advancing LLM Expertise in Technology Judgment Siyang Wu Honglin Bao Nadav Kunievsky James A. Evans 17 0 0 18 May 2025
Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces Michael Pichat William Pogrund Paloma Pichat Judicael Poumay Armanouche Gasparian Samuel Demarchi Martin Corbet Alois Georgeon Michael Veillet-Guillem MILM 29 0 0 30 Apr 2025
Composable Interventions for Language Models Arinbjorn Kolbeinsson Kyle O'Brien Tianjin Huang Shanghua Gao Shiwei Liu ... Anurag J. Vaidya Faisal Mahmood Marinka Zitnik Tianlong Chen Thomas Hartvigsen KELM MU 89 5 0 09 Jul 2024
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning Xiangyu Peng Chen Xing Prafulla Kumar Choubey Chien-Sheng Wu Caiming Xiong VLM 86 11 0 23 Oct 2022
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,872 0 18 Apr 2021