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

• URL: http://arxiv.org/abs/2412.00281v1
• Date: Fri, 29 Nov 2024 23:26:34 GMT
• Title: Streamlining the review process: AI-generated annotations in research manuscripts
• Authors: Oscar Díaz, Xabier Garmendia, Juanan Pereira,
• Abstract summary: This study explores the potential of integrating Large Language Models (LLMs) into the peer-review process to enhance efficiency without compromising effectiveness.<n>We focus on manuscript annotations, particularly excerpt highlights, as a potential area for AI-human collaboration.<n>This paper introduces AnnotateGPT, a platform that utilizes GPT-4 for manuscript review, aiming to improve reviewers' comprehension and focus.
• Score: 0.5735035463793009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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