面向人力资源的代理AI：基于大语言模型的候选人评估
Agentic AI for Human Resources: LLM-Driven Candidate Assessment
AI总结：
本文提出一个模块化且可解释的框架，利用大语言模型自动化招聘中的候选人评估。系统整合多种来源生成结构化评估报告，采用LLM生成的评分标准和多代理架构进行细粒度评估，输出透明、可审计的评估报告和推荐。
摘要：
本文提出一个模块化且可解释的框架，利用大语言模型自动化招聘中的候选人评估。系统整合多种来源，包括职位描述、简历、面试记录和人力资源反馈，生成与专家判断相似的结构化评估报告。不同于传统ATS工具依赖关键词匹配或浅层评分，我们的方法采用基于角色的LLM生成评分标准和多代理架构进行细粒度、基于标准的评估。框架输出详细的评估报告、候选人比较和排名推荐，这些结果是透明、可审计且适用于现实招聘流程的。除了基于评分标准的分析，我们引入了一种由LLM驱动的主动列表wise竞赛机制用于候选人排名。而不是嘈杂的成对比较或不一致的独立评分，LLM对小的候选人子集（迷你竞赛）进行排名，这些列表排列通过Plackett-Luce模型聚合。主动学习循环选择最有信息量的子集，产生全球一致且样本高效排名。这种对列表wise LLM 偏好建模（此前在金融资产排名中已探索）的适应，为大规模人才招聘中的候选人排名提供了系统且高度可解释的方法。
英文摘要：
In this work, we present a modular and interpretable framework that uses Large Language Models (LLMs) to automate candidate assessment in recruitment. The system integrates diverse sources, including job descriptions, CVs, interview transcripts, and HR feedback; to generate structured evaluation reports that mirror expert judgment. Unlike traditional ATS tools that rely on keyword matching or shallow scoring, our approach employs role-specific, LLM-generated rubrics and a multi-agent architecture to perform fine-grained, criteria-driven evaluations. The framework outputs detailed assessment reports, candidate comparisons, and ranked recommendations that are transparent, auditable, and suitable for real-world hiring workflows. Beyond rubric-based analysis, we introduce an LLM-Driven Active Listwise Tournament mechanism for candidate ranking. Instead of noisy pairwise comparisons or inconsistent independent scoring, the LLM ranks small candidate subsets (mini-tournaments), and these listwise permutations are aggregated using a Plackett-Luce model. An active-learning loop selects the most informative subsets, producing globally coherent and sample-efficient rankings. This adaptation of listwise LLM preference modeling (previously explored in financial asset ranking) provides a principled and highly interpretable methodology for large-scale candidate ranking in talent acquisition.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
