Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models

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.

Artificial Intelligence (cs.AI); Digital Libraries (cs.DL); Information Retrieval (cs.IR)

arXiv:2608.26150 [cs.AI]

(or arXiv:2608.26150v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.26150 Focus to learn more arXiv-issued DOI via DataCite

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