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

• URL: http://arxiv.org/abs/2604.00006v1
• Date: Mon, 09 Mar 2026 13:15:46 GMT
• Title: Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models
• Abstract summary: We propose a large language model (LLM)-based approach to identify and prioritize req-specific PCs from reqs.<n>Our approach integrates dynamic few-shot prompting, reflection-based self-improvement, similarity-based filtering, and multi-stage validation.
• Score: 1.8148613308949546
• License: http://creativecommons.org/licenses/by/4.0/
• 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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