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 4%∼36% compared to the finetuned 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.
Preprint version Young Researchers Symposium, CODS-COMAD 2024, 2023.
© Shiri, Roy, Sheth, & Gaur | ACM. 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution.
Shiri, A., Roy, K., Sheth, A., & Gaur, M. (2023). L3 ensembles: Lifelong learning approach for ensemble of foundational language models*. Young Researchers Symposium, CODS-COMAD 2024. [Preprint]