@inproceedings{hossain-etal-2025-llms,
title = "{LLM}s as Meta-Reviewers' Assistants: A Case Study",
author = "Hossain, Eftekhar and
Sinha, Sanjeev Kumar and
Bansal, Naman and
Knipper, R. Alexander and
Sarkar, Souvika and
Salvador, John and
Mahajan, Yash and
Guttikonda, Sri Ram Pavan Kumar and
Akter, Mousumi and
Hassan, Md. Mahadi and
Freestone, Matthew and
Jr., Matthew C. Williams and
Feng, Dongji and
Karmaker, Santu",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.395/",
doi = "10.18653/v1/2025.naacl-long.395",
pages = "7763--7803",
ISBN = "979-8-89176-189-6",
abstract = "One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peers, formulating one{'}s self-judgment as a senior expert, and then summarizing all these perspectives into a concise holistic overview to make an overall recommendation. This process is time-consuming and can be compromised by human factors like fatigue, inconsistency, missing tiny details, etc. Given the latest major developments in Large Language Models (LLMs), it is very compelling to rigorously study whether LLMs can help meta-reviewers perform this important task better. In this paper, we perform a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and PaLM2, to assist meta-reviewers in better comprehending multiple experts' perspectives by generating a controlled multi-perspective-summary (MPS) of their opinions. To achieve this, we prompt three LLMs with different types/levels of prompts based on the recently proposed TELeR taxonomy. Finally, we perform a detailed qualitative study of the MPSs generated by the LLMs and report our findings."
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<abstract>One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peers, formulating one’s self-judgment as a senior expert, and then summarizing all these perspectives into a concise holistic overview to make an overall recommendation. This process is time-consuming and can be compromised by human factors like fatigue, inconsistency, missing tiny details, etc. Given the latest major developments in Large Language Models (LLMs), it is very compelling to rigorously study whether LLMs can help meta-reviewers perform this important task better. In this paper, we perform a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and PaLM2, to assist meta-reviewers in better comprehending multiple experts’ perspectives by generating a controlled multi-perspective-summary (MPS) of their opinions. To achieve this, we prompt three LLMs with different types/levels of prompts based on the recently proposed TELeR taxonomy. Finally, we perform a detailed qualitative study of the MPSs generated by the LLMs and report our findings.</abstract>
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%0 Conference Proceedings
%T LLMs as Meta-Reviewers’ Assistants: A Case Study
%A Hossain, Eftekhar
%A Sinha, Sanjeev Kumar
%A Bansal, Naman
%A Knipper, R. Alexander
%A Sarkar, Souvika
%A Salvador, John
%A Mahajan, Yash
%A Guttikonda, Sri Ram Pavan Kumar
%A Akter, Mousumi
%A Hassan, Md. Mahadi
%A Freestone, Matthew
%A Jr., Matthew C. Williams
%A Feng, Dongji
%A Karmaker, Santu
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F hossain-etal-2025-llms
%X One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peers, formulating one’s self-judgment as a senior expert, and then summarizing all these perspectives into a concise holistic overview to make an overall recommendation. This process is time-consuming and can be compromised by human factors like fatigue, inconsistency, missing tiny details, etc. Given the latest major developments in Large Language Models (LLMs), it is very compelling to rigorously study whether LLMs can help meta-reviewers perform this important task better. In this paper, we perform a case study with three popular LLMs, i.e., GPT-3.5, LLaMA2, and PaLM2, to assist meta-reviewers in better comprehending multiple experts’ perspectives by generating a controlled multi-perspective-summary (MPS) of their opinions. To achieve this, we prompt three LLMs with different types/levels of prompts based on the recently proposed TELeR taxonomy. Finally, we perform a detailed qualitative study of the MPSs generated by the LLMs and report our findings.
%R 10.18653/v1/2025.naacl-long.395
%U https://aclanthology.org/2025.naacl-long.395/
%U https://doi.org/10.18653/v1/2025.naacl-long.395
%P 7763-7803
Markdown (Informal)
[LLMs as Meta-Reviewers’ Assistants: A Case Study](https://aclanthology.org/2025.naacl-long.395/) (Hossain et al., NAACL 2025)
ACL
- Eftekhar Hossain, Sanjeev Kumar Sinha, Naman Bansal, R. Alexander Knipper, Souvika Sarkar, John Salvador, Yash Mahajan, Sri Ram Pavan Kumar Guttikonda, Mousumi Akter, Md. Mahadi Hassan, Matthew Freestone, Matthew C. Williams Jr., Dongji Feng, and Santu Karmaker. 2025. LLMs as Meta-Reviewers’ Assistants: A Case Study. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 7763–7803, Albuquerque, New Mexico. Association for Computational Linguistics.