The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. The advent of Large Language Models (LLMs) has notably enhanced the efficacy of these systems, showcasing their robust generalization abilities across diverse language-related tasks. Accompanying these developments are various agents based on LLMs, which facilitate their application in practical scenarios. This paper introduces a novel LLM-based agent framework for resume screening, aimed at enhancing efficiency and time management in recruitment processes. Our framework is distinct in its ability to efficiently summarize and grade each resume from a large dataset. Moreover, it utilizes LLM agents for decision-making, determining which candidates receive job offers, or which ones to bring in for interviews. To evaluate our framework, we constructed a dataset from actual resumes and conducted simulate a resume screening process. Subsequently, the outcomes of the simulation experiment were compared and subjected to detailed analysis. The results demonstrate that our automated resume screening framework is 11 times faster than traditional manual methods. Furthermore, by fine-tuning the LLMs, we observed a significant improvement in the F1 score, reaching 87.73\%, during the resume sentence classification phase. In the resume summarization and grading phase, our fine-tuned model surpassed the baseline performance of the GPT-3.5 model. Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.

翻译：简历筛选自动化是组织招聘流程中的关键环节。自动化简历筛选系统通常涵盖一系列自然语言处理任务。大型语言模型的出现显著提升了这些系统的效能，展现出其在跨语言任务中的强大泛化能力。伴随这些技术发展，基于大型语言模型的各种智能体得以出现，推动了其在实际场景中的应用。本文提出一种基于大型语言模型的新型智能体框架用于简历筛选，旨在提升招聘流程的效率与时间管理能力。该框架的独特之处在于能高效地对大规模数据集中的每份简历进行总结与评分。此外，它利用大型语言模型智能体进行决策，确定哪些候选人应获得工作录用或进入面试环节。为评估该框架，我们基于真实简历构建数据集，并模拟了简历筛选流程。随后，对模拟实验结果进行比较与详细分析。结果表明，该自动化简历筛选框架的速度比传统人工方法快11倍。此外，通过对大型语言模型进行微调，在简历句子分类阶段，F1分数显著提升至87.73%。在简历总结与评分阶段，我们微调后的模型超越了GPT-3.5模型的基线性能。最后阶段对大型语言模型智能体决策效能的分析进一步凸显了其在变革简历筛选流程方面的潜力。