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

Revising scientific papers based on peer feedback is a challenging task thatrequires not only deep scientific knowledge and reasoning, but also the abilityto recognize the implicit requests in high-level feedback and to choose thebest of many possible ways to update the manuscript in response. We introducethis task for large language models and release ARIES, a dataset of reviewcomments and their corresponding paper edits, to enable training and evaluatingmodels. We study two versions of the task: comment-edit alignment and editgeneration, and evaluate several baselines, including GPT-4. We find thatmodels struggle even to identify the edits that correspond to a comment,especially in cases where the comment is phrased in an indirect way or wherethe edit addresses the spirit of a comment but not the precise request. Whentasked with generating edits, GPT-4 often succeeds in addressing comments on asurface level, but it rigidly follows the wording of the feedback rather thanthe underlying intent, and includes fewer technical details than human-writtenedits. We hope that our formalization, dataset, and analysis will form afoundation for future work in this area.

ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews