Toward Human-Aligned LLM Reviews for Scientific Papers

The peer review process is strained by increasing submission volumes, reviewer fatigue, and inconsistent standards. While Large Language Models (LLMs) can aid in reviews, they are often overly optimistic and lack technical depth. We developed an innovative prompting strategy that, when applied to ChatGPT-4 on ICLR 2025 papers, reduced score inflation and generated reviews more closely aligned with human reviewer median scores.

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Publication Details

Published
2025-09-15
DOI
https://doi.org/10.1109/escience65000.2025.00072
Primary Topic
Artificial Intelligence in Law
Type
article
Field-Weighted Citation Impact
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article

Toward Human-Aligned LLM Reviews for Scientific Papers

Arash Mahyari, Ashok Srinivasan, Ranjitha Shivaprasad Ballakuraya, Brent Venable
Artificial Intelligence in Law
article

Toward Human-Aligned LLM Reviews for Scientific Papers

Arash Mahyari, Ashok Srinivasan, Ranjitha Shivaprasad Ballakuraya, Brent Venable
article en

Abstract

The peer review process is strained by increasing submission volumes, reviewer fatigue, and inconsistent standards. While Large Language Models (LLMs) can aid in reviews, they are often overly optimistic and lack technical depth. We developed an innovative prompting strategy that, when applied to ChatGPT-4 on ICLR 2025 papers, reduced score inflation and generated reviews more closely aligned with human reviewer median scores.

University of West Florida (US)
Openalex Percentile: Top 68%
Artificial Intelligence in Law
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Toward Human-Aligned LLM Reviews for Scientific Papers — Arash Mahyari, Ashok Srinivasan, et al. · (2025) | TGRS Research Map | TGRS