用大模型精准识别招聘需求中的关键个人能力
Scalable Identification and Prioritization of Requisition-Specific Personal Competencies Using Large Language Models
- 结合少样本提示与自我优化，动态提取岗位特异性能力
- 在项目经理岗位上准确率达76%，接近人工评估水平
- 适合人力资源自动化工具研发者与招聘系统优化者
AI驱动的招聘工具在人员选拔中应用日益广泛，但难以捕捉超越职位类别、决定候选人优劣的岗位特异性个人能力（PCs）。本文提出一种基于大语言模型（LLM）的方法，从岗位需求中识别并优先排序此类能力。方法融合动态少样本提示、基于反思的自我改进、相似性过滤及多阶段验证。应用于项目管理岗位需求数据集，该方法在识别最高优先级岗位特异性能力时平均准确率达0.76，接近人类专家间一致性水平，且错误外溢率低至0.07。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。