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

State-of-the-art language generation models can degenerate when applied toopen-ended generation problems such as text completion, story generation, ordialog modeling. This degeneration usually shows up in the form of incoherence,lack of vocabulary diversity, and self-repetition or copying from the context.In this paper, we postulate that ``human-like'' generations usually lie in anarrow and nearly flat entropy band, and violation of these entropy boundscorrelates with degenerate behavior. Our experiments show that this stablenarrow entropy zone exists across models, tasks, and domains and confirm thehypothesis that violations of this zone correlate with degeneration. We thenuse this insight to propose an entropy-aware decoding algorithm that respectsthese entropy bounds resulting in less degenerate, more contextual, and"human-like" language generation in open-ended text generation settings.

The Stable Entropy Hypothesis and Entropy-Aware Decoding: An Analysis and Algorithm for Robust Natural Language Generation