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arXiv · 2604.00006

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

Wanxin Li·Denver McNeney·Nivedita Prabhu·Charlene Zhang·Renee Barr·Matthew Kitching·Khanh Dao Duc·Anthony S. Boyce
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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.

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Wanxin Li, Denver McNeney, Nivedita Prabhu, Charlene Zhang, Renee Barr, Matthew Kitching, Khanh Dao Duc, Anthony S. Boyce. 2026-03-09. Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models. https://arxiv.org/abs/2604.00006

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