# Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews Source: [Leveraging LLMs for Semi-Automatic Corpus Filtration in Systematic Literature Reviews]() Collection index: [llms.txt]() Data updated: 2026-02-19T05:00:04.488Z Authors: Lucas Joos, Daniel A. Keim, Maximilian T. Fischer Published: 2025-10-13T13:48:29.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Paper Classification on Fall paper classification corpus 2025](): Paper Classification; Fall paper classification corpus 2025; TP (True Positives): 88. - [Corpus Filtering on SLR Survey Corpus Visual Network Analysis in Immersive Environments Preliminary (Full Corpus)](): Corpus Filtering; SLR Survey Corpus Visual Network Analysis in Immersive Environments Preliminary (Full Corpus); TP: 87. ## Original sources and implementation [Original paper]() [PDF]()