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

Despite the growing success of diffusion models in continuous-valued domains(e.g., images), similar efforts for discrete domains such as text have yet tomatch the performance of autoregressive language models. In this work, wepresent SSD-LM -- a diffusion-based language model with two key design choices.First, SSD-LM is semi-autoregressive, iteratively generating blocks of text,allowing for flexible output length at decoding time while enabling localbidirectional context updates. Second, it is simplex-based, performingdiffusion on the natural vocabulary space rather than a learned latent space,allowing us to incorporate classifier guidance and modular control usingoff-the-shelf classifiers without any adaptation. We evaluate SSD-LM onunconstrained text generation benchmarks, and show that it matches oroutperforms strong autoregressive GPT-2 models across standard quality anddiversity metrics, while vastly outperforming diffusion-based baselines. Oncontrolled text generation, SSD-LM also outperforms competitive baselines, withan extra advantage in modularity.

SSD-LM: Semi-autoregressive Simplex-based Diffusion Language Model for Text Generation and Modular Control