English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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

L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models