Recurrent Knowledge Identification and Fusion for Language Model Continual Learning

TL;DR

Recurrent-KIF introduces a dynamic continual learning framework for large language models that adaptively estimates parameter importance, improving knowledge transfer and reducing forgetting through iterative inner-outer loop fusion strategies.

Contribution

It proposes a novel Recurrent-KIF framework that dynamically estimates parameter importance and iteratively fuses knowledge, outperforming static importance methods in continual learning.

Findings

Recurrent-KIF effectively mitigates catastrophic forgetting.

It improves knowledge transfer across various model sizes.

Experimental results outperform existing methods on benchmark tasks.

Abstract

Continual learning (CL) is crucial for deploying large language models (LLMs) in dynamic real-world environments without costly retraining. While recent model ensemble and model merging methods guided by parameter importance have gained popularity, they often struggle to balance knowledge transfer and forgetting, mainly due to the reliance on static importance estimates during sequential training. In this paper, we present Recurrent-KIF, a novel CL framework for Recurrent Knowledge Identification and Fusion, which enables dynamic estimation of parameter importance distributions to enhance knowledge transfer. Inspired by human continual learning, Recurrent-KIF employs an inner loop that rapidly adapts to new tasks while identifying important parameters, coupled with an outer loop that globally manages the fusion of new and historical knowledge through redundant knowledge pruning and key knowledge merging. These inner-outer loops iteratively perform multiple rounds of fusion, allowing Recurrent-KIF to leverage intermediate training information and adaptively adjust fusion strategies based on evolving importance distributions. Extensive experiments on two CL benchmarks with various model sizes (from 770M to 13B) demonstrate that Recurrent-KIF effectively mitigates catastrophic forgetting and enhances knowledge transfer.