  Pretraining on a large-scale corpus has become a standard method to build
general language models (LMs). Adapting a model to new data distributions
targeting different downstream tasks poses significant challenges. Naive
fine-tuning may incur catastrophic forgetting when the over-parameterized LMs
overfit the new data but fail to preserve the pretrained features. Lifelong
learning (LLL) aims to enable information systems to learn from a continuous
data stream across time. However, most prior work modifies the training recipe
assuming a static fixed network architecture. We find that additional model
capacity and proper regularization are key elements to achieving strong LLL
performance. Thus, we propose Lifelong-MoE, an extensible MoE
(Mixture-of-Experts) architecture that dynamically adds model capacity via
adding experts with regularized pretraining. Our results show that by only
introducing a limited number of extra experts while keeping the computation
cost constant, our model can steadily adapt to data distribution shifts while
preserving the previous knowledge. Compared to existing lifelong learning
approaches, Lifelong-MoE achieves better few-shot performance on 19 downstream
NLP tasks.
