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

We study the ability of LLMs to generate feedback for scientific papers anddevelop MARG, a feedback generation approach using multiple LLM instances thatengage in internal discussion. By distributing paper text across agents, MARGcan consume the full text of papers beyond the input length limitations of thebase LLM, and by specializing agents and incorporating sub-tasks tailored todifferent comment types (experiments, clarity, impact) it improves thehelpfulness and specificity of feedback. In a user study, baseline methodsusing GPT-4 were rated as producing generic or very generic comments more thanhalf the time, and only 1.7 comments per paper were rated as good overall inthe best baseline. Our system substantially improves the ability of GPT-4 togenerate specific and helpful feedback, reducing the rate of generic commentsfrom 60% to 29% and generating 3.7 good comments per paper (a 2.2ximprovement).

MARG: Multi-Agent Review Generation for Scientific Papers