AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening

• URL: http://arxiv.org/abs/2504.02870v1
• Date: Tue, 01 Apr 2025 12:56:39 GMT
• Title: AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening
• Authors: Frank P. -W. Lo, Jianing Qiu, Zeyu Wang, Haibao Yu, Yeming Chen, Gao Zhang, Benny Lo,
• Abstract summary: We propose a multi-agent framework for resume screening using Large Language Models (LLMs)<n>The framework consists of four core agents, including a resume extractor, an evaluator, a summarizer, and a score formatter.<n>This dynamic adaptation enables personalized recruitment, bridging the gap between AI automation and talent acquisition.
• Score: 12.845918958645676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications while remaining objective, accurate, and fair. With the advancements in Large Language Models (LLMs), their reasoning capabilities and extensive knowledge bases demonstrate new opportunities to streamline and automate recruitment workflows. In this work, we propose a multi-agent framework for resume screening using LLMs to systematically process and evaluate resumes. The framework consists of four core agents, including a resume extractor, an evaluator, a summarizer, and a score formatter. To enhance the contextual relevance of candidate assessments, we integrate Retrieval-Augmented Generation (RAG) within the resume evaluator, allowing incorporation of external knowledge sources, such as industry-specific expertise, professional certifications, university rankings, and company-specific hiring criteria. This dynamic adaptation enables personalized recruitment, bridging the gap between AI automation and talent acquisition. We assess the effectiveness of our approach by comparing AI-generated scores with ratings provided by HR professionals on a dataset of anonymized online resumes. The findings highlight the potential of multi-agent RAG-LLM systems in automating resume screening, enabling more efficient and scalable hiring workflows.

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