Accelerating Discovery: Rapid Literature Screening with LLMs

• URL: http://arxiv.org/abs/2509.13103v1
• Date: Tue, 16 Sep 2025 14:01:44 GMT
• Title: Accelerating Discovery: Rapid Literature Screening with LLMs
• Authors: Santiago Matalonga, Domenico Amalfitano, Jean Carlo Rossa Hauck, Martín Solari, Guilherme H. Travassos,
• Abstract summary: Researchers must review and filter a large number of unstructured sources, which frequently contain sparse information.<n>We developed a Large Language Model (LLM) assistant to support the search and filtering of documents.
• Score: 1.2586771241101986
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Background: Conducting Multi Vocal Literature Reviews (MVLRs) is often time and effort-intensive. Researchers must review and filter a large number of unstructured sources, which frequently contain sparse information and are unlikely to be included in the final study. Our experience conducting an MVLR on Context-Aware Software Systems (CASS) Testing in the avionics domain exemplified this challenge, with over 8,000 highly heterogeneous documents requiring review. Therefore, we developed a Large Language Model (LLM) assistant to support the search and filtering of documents. Aims: To develop and validate an LLM based tool that can support researchers in performing the search and filtering of documents for an MVLR without compromising the rigor of the research protocol. Method: We applied sound engineering practices to develop an on-premises LLM-based tool incorporating Retrieval Augmented Generation (RAG) to process candidate sources. Progress towards the aim was quantified using the Positive Percent Agreement (PPA) as the primary metric to ensure the performance of the LLM based tool. Convenience sampling, supported by human judgment and statistical sampling, were used to verify and validate the tool's quality-in-use. Results: The tool currently demonstrates a PPA agreement with human researchers of 90% for sources that are not relevant to the study. Development details are shared to support domain-specific adaptation of the tool. Conclusions: Using LLM-based tools to support academic researchers in rigorous MVLR is feasible. These tools can free valuable time for higher-level, abstract tasks. However, researcher participation remains essential to ensure that the tool supports thorough research.

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