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

To what extent do pre-trained language models grasp semantic knowledgeregarding the phenomenon of distributivity? In this paper, we introduceDistNLI, a new diagnostic dataset for natural language inference that targetsthe semantic difference arising from distributivity, and employ the causalmediation analysis framework to quantify the model behavior and explore theunderlying mechanism in this semantically-related task. We find that the extentof models' understanding is associated with model size and vocabulary size. Wealso provide insights into how models encode such high-level semanticknowledge.

Testing Pre-trained Language Models' Understanding of Distributivity via Causal Mediation Analysis