PeerArg: argumentative peer review with LLMs
File(s)
Accepted version
Author(s)
Sukpanichnant, Purin
Rapberger, Anna
Toni, Francesca
Type
Conference Paper
Abstract
Peer review is an essential process to determine the quality of papers submitted to scientific conferences or journals. However, it is subjective and prone to biases. Several studies have been conducted to apply techniques from NLP to support peer review, but they are based on black-box techniques and their outputs are difficult to interpret and trust. In this paper, we propose a novel pipeline to support and understand the reviewing and decision-making processes of peer review: the PeerArg system combining LLMs with methods from knowledge representation. PeerArg takes in input a set of reviews for a paper and outputs the paper acceptance prediction. We evaluate the performance of the PeerArg pipeline on three different datasets, in comparison with a novel end-2-end LLM that uses few-shot learning to predict paper acceptance given reviews. The results indicate that the end-2-end LLM is capable of predicting paper acceptance from reviews, but a variant
of the PeerArg pipeline outperforms this LLM.
of the PeerArg pipeline outperforms this LLM.
Date Acceptance
2024-08-24
Copyright Statement
This paper is embargoed until publication.
Source
First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024)
Publication Status
Accepted
Start Date
2024-11-02
Finish Date
2024-11-08
Coverage Spatial
Hanoi, Vietnam
Rights Embargo Date
10000-01-01