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

The use of positional embeddings in transformer language models is widelyaccepted. However, recent research has called into question the necessity ofsuch embeddings. We further extend this inquiry by demonstrating that arandomly initialized and frozen transformer language model, devoid ofpositional embeddings, inherently encodes strong positional information throughthe shrinkage of self-attention variance. To quantify this variance, we derivethe underlying distribution of each step within a transformer layer. Throughempirical validation using a fully pretrained model, we show that the varianceshrinkage effect still persists after extensive gradient updates. Our findingsserve to justify the decision to discard positional embeddings and thusfacilitate more efficient pretraining of transformer language models.

Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings