待翻译：Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models

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--> [Submitted on 1 Jul 2026] Title:Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models View a PDF of the paper titled Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models, by Orhan Yagizer Cinar and 5 other authors View PDF HTML (experimental) Abstract:Recent advancements in Large Language Models (LLMs) have created new opportunities to streamline and potentially automate many research processes, including systematic literature reviews (SLRs). This study reports an LLM pipeline development for extracting model-relevant information from 536 peer-reviewed agent-based modeling papers. We compare the results with those of a human-conducted SLR. Our results show paper-level accuracies of approximately 77.95% for GPT-4.1 and 81.67% for GPT-5.0. Field-level accuracy ranges from 32.40% to 100.00%, with more complex or subjective fields performing less reliably. Importantly, we find that agreement between LLMs is a potential indicator of output quality: low agreement may signal hallucinations, whereas high agreement combined with low accuracy may point to noise or errors in the human dataset. Overall, our study provides practical insights into prompt development and highlights both the potential and limitations of using LLMs for full-scale SLRs in the modeling and simulation domain. Comments: To be published in the Winter Simulation Conference 2026 Subjects: Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Information Retrieval (cs.IR) MSC classes: 68T50 ACM classes: I.2.7 Cite as: arXiv:2608.26150 [cs.AI] (or arXiv:2608.26150v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.26150 arXiv-issued DOI via DataCite Submission history From: Hamdi Kavak [view email] [v1] Wed, 1 Jul 2026 15:15:16 UTC (528 KB) Full-text links: Access Paper: View a PDF of the paper titled Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models, by Orhan Yagizer Cinar and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs cs.DL cs.IR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)