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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

While transferring a pretrained language model, common approachesconventionally attach their task-specific classifiers to the top layer andadapt all the pretrained layers. We investigate whether one could make atask-specific selection on which subset of the layers to adapt and where toplace the classifier. The goal is to reduce the computation cost of transferlearning methods (e.g. fine-tuning or adapter-tuning) without sacrificing itsperformance.  We propose to select layers based on the variability of their hidden statesgiven a task-specific corpus. We say a layer is already "well-specialized" in atask if the within-class variability of its hidden states is low relative tothe between-class variability. Our variability metric is cheap to compute anddoesn't need any training or hyperparameter tuning. It is robust to dataimbalance and data scarcity. Extensive experiments on the GLUE benchmarkdemonstrate that selecting layers based on our metric can yield significantlystronger performance than using the same number of top layers and often matchthe performance of fine-tuning or adapter-tuning the entire language model.

Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning