  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.
