[SWJ] Review received, #3810-5024

[www.semantic-web-journal.net]

A review has been provided for a paper which was assigned to you.

Authors: Sefika Efeoglu, Adrian Paschke1
Title: Retrieval-Augmented Generation-based Relation Extraction
Submission Type: 'Full Paper'
URL:
https://www.semantic-web-journal.net/content/retrieval-augmented-generation-based-relation-extraction-0
Tracking number: 3810-5024
Assigned editor: Guest Editors KG Gen from Text 2023
(text2...@googlegroups.com)
Review submitted by: Tek Raj Chhetri
Suggested decision: Accept

Review Comments:
I see few typos, please review for the finalized version and also would be
nice to see the discussion on ethical aspect.

Comments for editor:

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[www.semantic-web-journal.net]

A review has been provided for a paper which was assigned to you.

Authors: Sefika Efeoglu, Adrian Paschke1
Title: Retrieval-Augmented Generation-based Relation Extraction
Submission Type: 'Full Paper'
URL:
https://www.semantic-web-journal.net/content/retrieval-augmented-generation-based-relation-extraction-0
Tracking number: 3810-5024
Assigned editor: Guest Editors KG Gen from Text 2023
(text2...@googlegroups.com)

Review submitted by: Fatima Zahra

Suggested decision: Accept

Review Comments:

The paper "Retrieval-Augmented Generation-based Relation Extraction (RAG4RE)"
presents a novel zero-shot approach to relation extraction that enhances
prompt quality for large language models (LLMs) by integrating semantically
similar sentences retrieved from training data. This RAG-based framework,
evaluated on benchmark datasets such as TACRED, TACREV, Re-TACRED, and
SemEval using models like Flan-T5, LLaMA2, and Mistral, demonstrates superior
performance over simple query-based prompting and several state-of-the-art
methods, particularly in reducing hallucinations and improving micro-F1
scores. The authors detail a well-structured pipeline consisting of
retrieval, data augmentation, and generation modules, and support their
claims with comprehensive ablation studies. However, the approach shows
limited generalization to the SemEval dataset, likely due to its dependence
on contextually inferable relations and the limitations of vanilla LLMs.
While the method is robust and innovative, further refinements—such as
improved domain adaptation and more accurate retrieval—could enhance its
applicability across diverse relation extraction tasks.