LLMs Streamline Systematic Literature Reviews for Disease Models
Key takeaways
- LLMs can significantly streamline systematic literature reviews, achieving high paper-level accuracy.
- Accuracy varies by field complexity, with subjective fields performing less reliably.
- LLM agreement can indicate output quality, helping identify hallucinations or data errors.
- Human oversight remains essential for validating and refining LLM-generated content in SLRs.
Who benefits
Summary
This study develops an LLM pipeline for extracting information from agent-based modeling papers for systematic literature reviews (SLRs) on disease spread models. It achieved paper-level accuracies of 77.95% for GPT-4.1 and 81.67% for GPT-5.0, highlighting LLMs' potential to automate research processes while noting limitations in complex or subjective fields.
Why it matters
Researchers and analysts can significantly accelerate the initial stages of systematic literature reviews by leveraging LLMs, freeing up human experts for more complex synthesis and critical evaluation. Understanding LLM limitations is crucial for effective implementation.
How to implement this in your domain
- 1Develop structured prompts for LLMs to extract specific information from research papers.
- 2Pilot LLM-based information extraction on a small subset of papers to refine prompts and evaluate accuracy.
- 3Implement a multi-LLM approach to cross-validate outputs and identify potential hallucinations or errors.
- 4Integrate human review and correction workflows for complex or subjective data fields.
- 5Utilize LLMs to generate initial summaries or data points, then have human experts perform the final synthesis and critical analysis.
Original post by Orhan Yagizer Cinar, Timur Emre Ozkose, Emma Von Hoene, Amira Roess, Taylor Anderson, Hamdi Kavak
"arXiv:2608.26150v1 Announce Type: new 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 de…"
View on XOriginally posted by Orhan Yagizer Cinar, Timur Emre Ozkose, Emma Von Hoene, Amira Roess, Taylor Anderson, Hamdi Kavak on X · view source
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