[2604.00006] Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models

Computer Science > Computation and Language arXiv:2604.00006 (cs) [Submitted on 9 Mar 2026] Title:Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models Authors:Wanxin Li, Denver McNeney, Nivedita Prabhu, Charlene Zhang, Renee Barr, Matthew Kitching, Khanh Dao Duc, Anthony S. Boyce View a PDF of the paper titled Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models, by Wanxin Li and 7 other authors View PDF HTML (experimental) Abstract:AI-powered recruitment tools are increasingly adopted in personnel selection, yet they struggle to capture the requisition (req)-specific personal competencies (PCs) that distinguish successful candidates beyond job categories. We propose a large language model (LLM)-based approach to identify and prioritize req-specific PCs from reqs. Our approach integrates dynamic few-shot prompting, reflection-based self-improvement, similarity-based filtering, and multi-stage validation. Applied to a dataset of Program Manager reqs, our approach correctly identifies the highest-priority req-specific PCs with an average accuracy of 0.76, approaching human expert inter-rater reliability, and maintains a low out-of-scope rate of 0.07. Subjects: Computation and Language (cs.CL); Computers and Society (cs.CY); Information Retrieval (cs.IR); Machine Learning (cs.LG) Cite as: arXiv:2604.00006 [cs.CL]   (or arXiv:2604.00006v1 [cs.CL] for ...