TY  - EJOU
AU  - Chen, Ke 
AU  - Peng, Cheng 
AU  - He, Xinyang 
AU  - Sun, Jiakang 
AU  - Liu, Xu 
AU  - Qin, Xiaolin 
AU  - Zhong, Yong 

TI  - AdaptForever: Elastic and Mutual Learning for Continuous NLP Task Mastery
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 82
IS  - 3
SN  - 1546-2226

AB  - In natural language processing (NLP), managing multiple downstream tasks through fine-tuning pre-trained models often requires maintaining separate task-specific models, leading to practical inefficiencies. To address this challenge, we introduce AdaptForever, a novel approach that enables continuous mastery of NLP tasks through the integration of elastic and mutual learning strategies with a stochastic expert mechanism. Our method freezes the pre-trained model weights while incorporating adapters enhanced with mutual learning capabilities, facilitating effective knowledge transfer from previous tasks to new ones. By combining Elastic Weight Consolidation (EWC) for knowledge preservation with specialized regularization terms, AdaptForever successfully maintains performance on earlier tasks while acquiring new capabilities. Experimental results demonstrate that AdaptForever achieves superior performance across a continuous sequence of NLP tasks compared to existing parameter-efficient methods, while effectively preventing catastrophic forgetting and enabling positive knowledge transfer between tasks.
KW  - Adapter-tuning; large language model; pre-trained language model; parameter-efficient fine tuning; continue learning; mutual learning; mixture of expert

DO  - 10.32604/cmc.2025.057443