Combining established and emerging techniques to detect inconsistencies in requirements

Previous work has investigated the adequacy of LLMs to detect inconsistencies in requirements documents, but has also shown their limitations with real case studies. In this paper, we propose a hybrid approach, which exploits traditional clustering techniques to help LLMs focus on potential inconsistencies. The approach was evaluated using a large security requirements document from the RE Open Data Initiative, with injected inconsistencies. Results show that combining LLM-based detection with rule-based clustering enhances both precision and recall.