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

Since the introduction of the transformer model by Vaswani et al. (2017), afundamental question has yet to be answered: how does a model achieveextrapolation at inference time for sequences that are longer than it sawduring training? We first show that extrapolation can be enabled by simplychanging the position representation method, though we find that currentmethods do not allow for efficient extrapolation. We therefore introduce asimpler and more efficient position method, Attention with Linear Biases(ALiBi). ALiBi does not add positional embeddings to word embeddings; instead,it biases query-key attention scores with a penalty that is proportional totheir distance. We show that this method trains a 1.3 billion parameter modelon input sequences of length 1024 that extrapolates to input sequences oflength 2048, achieving the same perplexity as a sinusoidal position embeddingmodel trained on inputs of length 2048 but training 11% faster and using 11%less memory. ALiBi's inductive bias towards recency also leads it to outperformmultiple strong position methods on the WikiText-103 benchmark.

Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation