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

Instruction-following language models demand robust methodologies forinformation retrieval to augment instructions for question-answeringapplications. A primary challenge is the resolution of coreferences in thecontext of chunking strategies for long documents. The critical barrier toexperimentation of handling coreferences is a lack of open source datasets,specifically in question-answering tasks that require coreference resolution.In this work we present our Coreference Resolution in Question-Answering(CRaQAn) dataset, an open-source dataset that caters to the nuanced informationretrieval requirements of coreference resolution in question-answering tasks byproviding over 250 question-answer pairs containing coreferences. To developthis dataset, we developed a novel approach for creating high-quality datasetsusing an instruction-following model (GPT-4) and a Recursive Criticism andImprovement Loop.

Releasing the CRaQAn (Coreference Resolution in Question-Answering): An  open-source dataset and dataset creation methodology using  instruction-following models