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

We present a setup for training, evaluating and interpreting neural languagemodels, that uses artificial, language-like data. The data is generated using amassive probabilistic grammar (based on state-split PCFGs), that is itselfderived from a large natural language corpus, but also provides us completecontrol over the generative process. We describe and release both grammar andcorpus, and test for the naturalness of our generated data. This approachallows us to define closed-form expressions to efficiently compute exact lowerbounds on obtainable perplexity using both causal and masked languagemodelling. Our results show striking differences between neural languagemodelling architectures and training objectives in how closely they allowapproximating the lower bound on perplexity. Our approach also allows us todirectly compare learned representations to symbolic rules in the underlyingsource. We experiment with various techniques for interpreting model behaviourand learning dynamics. With access to the underlying true source, our resultsshow striking differences and outcomes in learning dynamics between differentclasses of words.

Transparency at the Source: Evaluating and Interpreting Language Models With Access to the True Distribution