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

Language model (LM) prompting--a popular paradigm for solving NLP tasks--hasbeen shown to be susceptible to miscalibration and brittleness to slight promptvariations, caused by its discriminative prompting approach, i.e., predictingthe label given the input. To address these issues, we propose Gen-Z--agenerative prompting framework for zero-shot text classification. GEN-Z isgenerative, as it measures the LM likelihood of input text, conditioned onnatural language descriptions of labels. The framework is multivariate, aslabel descriptions allow us to seamlessly integrate additional contextualinformation about the labels to improve task performance. On various standardclassification benchmarks, with six open-source LM families, we show thatzero-shot classification with simple contextualization of the data source ofthe evaluation set consistently outperforms both zero-shot and few-shotbaselines while improving robustness to prompt variations. Further, ourapproach enables personalizing classification in a zero-shot manner byincorporating author, subject, or reader information in the label descriptions.

Gen-Z: Generative Zero-Shot Text Classification with Contextualized Label Descriptions