Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview

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dc.citation.issue19
dc.citation.volume14
dc.contributor.authorHan B
dc.contributor.authorSusnjak T
dc.contributor.authorMathrani A
dc.contributor.editorGarcia Villalba LJ
dc.date.accessioned2024-10-29T01:02:26Z
dc.date.available2024-10-29T01:02:26Z
dc.date.issued2024-10-09
dc.description.abstractThis study examines Retrieval-Augmented Generation (RAG) in large language models (LLMs) and their significant application for undertaking systematic literature reviews (SLRs). RAG-based LLMs can potentially automate tasks like data extraction, summarization, and trend identification. However, while LLMs are exceptionally proficient in generating human-like text and interpreting complex linguistic nuances, their dependence on static, pre-trained knowledge can result in inaccuracies and hallucinations. RAG mitigates these limitations by integrating LLMs’ generative capabilities with the precision of real-time information retrieval. We review in detail the three key processes of the RAG framework—retrieval, augmentation, and generation. We then discuss applications of RAG-based LLMs to SLR automation and highlight future research topics, including integration of domain-specific LLMs, multimodal data processing and generation, and utilization of multiple retrieval sources. We propose a framework of RAG-based LLMs for automating SRLs, which covers four stages of SLR process: literature search, literature screening, data extraction, and information synthesis. Future research aims to optimize the interaction between LLM selection, training strategies, RAG techniques, and prompt engineering to implement the proposed framework, with particular emphasis on the retrieval of information from individual scientific papers and the integration of these data to produce outputs addressing various aspects such as current status, existing gaps, and emerging trends.
dc.description.confidentialfalse
dc.edition.editionOctober-1 2024
dc.identifier.citationHan B, Susnjak T, Mathrani A. (2024). Automating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview. Applied Sciences (Switzerland). 14. 19.
dc.identifier.doi10.3390/app14199103
dc.identifier.eissn2076-3417
dc.identifier.elements-typejournal-article
dc.identifier.number9103
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71855
dc.languageEnglish
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/2076-3417/14/19/9103
dc.relation.isPartOfApplied Sciences (Switzerland)
dc.rights(c) 2024 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectretrieval-augmented generation
dc.subjectlarge language models
dc.subjectsystematic literature review
dc.titleAutomating Systematic Literature Reviews with Retrieval-Augmented Generation: A Comprehensive Overview
dc.typeJournal article
pubs.elements-id492028
pubs.organisational-groupCollege of Health
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