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

There is a growing need to gain insight into language model capabilities thatrelate to sensitive topics, such as bioterrorism or cyberwarfare. However,traditional open source benchmarks are not fit for the task, due to theassociated practice of publishing the correct answers in human-readable form.At the same time, enforcing mandatory closed-quarters evaluations might stifledevelopment and erode trust. In this context, we propose hashmarking, aprotocol for evaluating language models in the open without having to disclosethe correct answers. In its simplest form, a hashmark is a benchmark whosereference solutions have been cryptographically hashed prior to publication.Following an overview of the proposed evaluation protocol, we go on to assessits resilience against traditional attack vectors (e.g. rainbow table attacks),as well as against failure modes unique to increasingly capable generativemodels.

Hashmarks: Privacy-Preserving Benchmarks for High-Stakes AI Evaluation