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

Autoregressive models for text sometimes generate repetitive and low-qualityoutput because errors accumulate during the steps of generation. This issue isoften attributed to exposure bias - the difference between how a model istrained, and how it is used during inference. Denoising diffusion modelsprovide an alternative approach in which a model can revisit and revise itsoutput. However, they can be computationally expensive and prior efforts ontext have led to models that produce less fluent output compared toautoregressive models, especially for longer text and paragraphs. In thispaper, we propose PLANNER, a model that combines latent semantic diffusion withautoregressive generation, to generate fluent text while exercising globalcontrol over paragraphs. The model achieves this by combining an autoregressive"decoding" module with a "planning" module that uses latent diffusion togenerate semantic paragraph embeddings in a coarse-to-fine manner. The proposedmethod is evaluated on various conditional generation tasks, and results onsemantic generation, text completion and summarization show its effectivenessin generating high-quality long-form text in an efficient manner.

PLANNER: Generating Diversified Paragraph via Latent Language Diffusion Model