From Static to Dynamic: A Continual Learning Framework for Large
  Language Models
Mingzhe Du Anh Tuan Luu Bin Ji See-kiong Ng
The vast number of parameters in large language models (LLMs) endows them
with remarkable capabilities, allowing them to excel in a variety of natural
language processing tasks. However, this complexity also presents challenges,
making LLMs difficult to train and inhibiting their ability to continuously
assimilate new knowledge, which may lead to inaccuracies in their outputs. To
mitigate these issues, this paper presents DynaMind, a novel continual learning
framework designed for LLMs. DynaMind incorporates memory mechanisms to
assimilate new knowledge and modular operators to enhance the model inference
process with the newly assimilated knowledge, consequently improving the
accuracies of LLMs' outputs. Benchmark experiments demonstrate DynaMind's
effectiveness in overcoming these challenges. The code and demo of DynaMind are
available on GitHub: https://github.com/Elfsong/DynaMind.