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APRES, a novel LLM-powered system, is introduced to revise scientific papers based on an automatically discovered rubric predictive of future citation counts. The system revises papers to enhance quality and impact without altering core scientific content, using a learned rubric to guide the revisions. Experiments demonstrate that APRES improves future citation prediction by 19.6% compared to baselines, and human evaluators prefer APRES-revised papers 79% of the time.
LLMs can revise scientific papers to significantly improve their predicted citation impact and perceived quality, suggesting a powerful new tool for authors to refine their manuscripts.
Scientific discoveries must be communicated clearly to realize their full potential. Without effective communication, even the most groundbreaking findings risk being overlooked or misunderstood. The primary way scientists communicate their work and receive feedback from the community is through peer review. However, the current system often provides inconsistent feedback between reviewers, ultimately hindering the improvement of a manuscript and limiting its potential impact. In this paper, we introduce a novel method APRES powered by Large Language Models (LLMs) to update a scientific papers text based on an evaluation rubric. Our automated method discovers a rubric that is highly predictive of future citation counts, and integrate it with APRES in an automated system that revises papers to enhance their quality and impact. Crucially, this objective should be met without altering the core scientific content. We demonstrate the success of APRES, which improves future citation prediction by 19.6% in mean averaged error over the next best baseline, and show that our paper revision process yields papers that are preferred over the originals by human expert evaluators 79% of the time. Our findings provide strong empirical support for using LLMs as a tool to help authors stress-test their manuscripts before submission. Ultimately, our work seeks to augment, not replace, the essential role of human expert reviewers, for it should be humans who discern which discoveries truly matter, guiding science toward advancing knowledge and enriching lives.
