Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews

• URL: http://arxiv.org/abs/2510.11409v1
• Date: Mon, 13 Oct 2025 13:48:29 GMT
• Title: Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews
• Authors: Lucas Joos, Daniel A. Keim, Maximilian T. Fischer,
• Abstract summary: We propose a pipeline leveraging multiple large language models (LLMs), classifying papers based on descriptive prompts and deciding jointly.<n>The entire process is human-supervised and interactively controlled via our open-source visual analytics web interface, LLMSurver.<n>Results demonstrate that our pipeline significantly reduces manual effort while achieving lower error rates than single human annotators.
• Score: 5.911820207772152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The creation of systematic literature reviews (SLR) is critical for analyzing the landscape of a research field and guiding future research directions. However, retrieving and filtering the literature corpus for an SLR is highly time-consuming and requires extensive manual effort, as keyword-based searches in digital libraries often return numerous irrelevant publications. In this work, we propose a pipeline leveraging multiple large language models (LLMs), classifying papers based on descriptive prompts and deciding jointly using a consensus scheme. The entire process is human-supervised and interactively controlled via our open-source visual analytics web interface, LLMSurver, which enables real-time inspection and modification of model outputs. We evaluate our approach using ground-truth data from a recent SLR comprising over 8,000 candidate papers, benchmarking both open and commercial state-of-the-art LLMs from mid-2024 and fall 2025. Results demonstrate that our pipeline significantly reduces manual effort while achieving lower error rates than single human annotators. Furthermore, modern open-source models prove sufficient for this task, making the method accessible and cost-effective. Overall, our work demonstrates how responsible human-AI collaboration can accelerate and enhance systematic literature reviews within academic workflows.

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