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

Prompt recently have become an effective linguistic tool on utilizing thepre-trained language models. However, in few-shot scenarios, subtle changes ofprompt's design always make the result widely different, and the prompt designis also easy to overfit the current limited samples. To alleviate this, weexplore how to utilize suitable contrastive samples and multiple contrastivelearning methods to realize a more robust prompt's representation. Therefore,the contrastive prompt model ConsPrompt combining with prompt encoding network,contrastive sampling modules, and contrastive scoring modules are introduced torealize differential contrastive learning. Our results exhibit thestate-of-the-art performance in different few-shot settings, and the ablationexperiments also certificate the effectiveness in utilizing multi-degreecontrastive learning in prompt-based fine-tuning process.

ConsPrompt: Exploiting Contrastive Samples for Fewshot Prompt Learning