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

Classifiers in natural language processing (NLP) often have a large number ofoutput classes. For example, neural language models (LMs) and machinetranslation (MT) models both predict tokens from a vocabulary of thousands. TheSoftmax output layer of these models typically receives as input a densefeature representation, which has much lower dimensionality than the output. Intheory, the result is some words may be impossible to be predicted via argmax,irrespective of input features, and empirically, there is evidence this happensin small language models. In this paper we ask whether it can happen inpractical large language models and translation models. To do so, we developalgorithms to detect such \emph{unargmaxable} tokens in public models. We findthat 13 out of 150 models do indeed have such tokens; however, they are veryinfrequent and unlikely to impact model quality. We release our code so thatothers can inspect their models.

Low-Rank Softmax Can Have Unargmaxable Classes in Theory but Rarely in Practice