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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Recent causal probing literature reveals when language models and syntacticprobes use similar representations. Such techniques may yield "false negative"causality results: models may use representations of syntax, but probes mayhave learned to use redundant encodings of the same syntactic information. Wedemonstrate that models do encode syntactic information redundantly andintroduce a new probe design that guides probes to consider all syntacticinformation present in embeddings. Using these probes, we find evidence for theuse of syntax in models where prior methods did not, allowing us to boost modelperformance by injecting syntactic information into representations.

When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes