  Fine-tuning pre-trained foundational language models (FLM) for specific tasks
is often impractical, especially for resource-constrained devices. This
necessitates the development of a Lifelong Learning (L3) framework that
continuously adapts to a stream of Natural Language Processing (NLP) tasks
efficiently. We propose an approach that focuses on extracting meaningful
representations from unseen data, constructing a structured knowledge base, and
improving task performance incrementally. We conducted experiments on various
NLP tasks to validate its effectiveness, including benchmarks like GLUE and
SuperGLUE. We measured good performance across the accuracy, training
efficiency, and knowledge transfer metrics. Initial experimental results show
that the proposed L3 ensemble method increases the model accuracy by 4% ~ 36%
compared to the fine-tuned FLM. Furthermore, L3 model outperforms naive
fine-tuning approaches while maintaining competitive or superior performance
(up to 15.4% increase in accuracy) compared to the state-of-the-art language
model (T5) for the given task, STS benchmark.
