# Streamlining the review process: AI-generated annotations in research manuscripts

arXiv: https://arxiv.org/abs/2412.00281 · HTML: https://mlsanity.com/paper/2412.00281/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 29 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations](https://mlsanity.com/paper/2310.11207/) (2023)
- [Can We Automate Scientific Reviewing?](https://mlsanity.com/paper/2102.00176/) (2021)
- [ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis](https://mlsanity.com/paper/2010.06119/) (2020)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)

## Built on by (most cited first, 2 of 2)

- [Large language models for automated scholarly paper review: A survey](https://mlsanity.com/paper/2501.10326/) (2025)
- [Beyond "Not Novel Enough": Enriching Scholarly Critique with LLM-Assisted Feedback](https://mlsanity.com/paper/2508.10795/) (2025)

## Abstract (arXiv)

The increasing volume of research paper submissions poses a significant challenge to the traditional academic peer-review system, leading to an overwhelming workload for reviewers. This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without compromising effectiveness. We focus on manuscript annotations, particularly excerpt highlights, as a potential area for AI-human collaboration. While LLMs excel in certain tasks like aspect coverage and informativeness, they often lack high-level analysis and critical thinking, making them unsuitable for replacing human reviewers entirely. Our approach involves using LLMs to assist with specific aspects of the review process. This paper introduces AnnotateGPT, a platform that utilizes GPT-4 for manuscript review, aiming to improve reviewers' comprehension and focus. We evaluate AnnotateGPT using a Technology Acceptance Model (TAM) questionnaire with nine participants and generalize the findings. Our work highlights annotation as a viable middle ground for AI-human collaboration in academic review, offering insights into integrating LLMs into the review process and tuning traditional annotation tools for LLM incorporation.