MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking

• URL: http://arxiv.org/abs/2507.10472v1
• Date: Mon, 14 Jul 2025 16:53:19 GMT
• Title: MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking
• Authors: Mohamed T. Younes, Omar Walid, Mai Hassan, Ali Hamdi,
• Abstract summary: This paper introduces an innovative Applicant Tracking System (ATS) enhanced by a novel Robotic process automation (RPA) framework or as further referred to as MLAR.<n>MLAR addresses these challenges employing Large Language Models (LLMs) in three distinct layers: extracting key characteristics from job postings in the first layer, parsing applicant resume to identify education, experience, skills in the second layer, and similarity matching in the third layer.<n>Our approach integrates seamlessly into existing RPA pipelines, automating resume parsing, job matching, and candidate notifications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper introduces an innovative Applicant Tracking System (ATS) enhanced by a novel Robotic process automation (RPA) framework or as further referred to as MLAR. Traditional recruitment processes often encounter bottlenecks in resume screening and candidate shortlisting due to time and resource constraints. MLAR addresses these challenges employing Large Language Models (LLMs) in three distinct layers: extracting key characteristics from job postings in the first layer, parsing applicant resume to identify education, experience, skills in the second layer, and similarity matching in the third layer. These features are then matched through advanced semantic algorithms to identify the best candidates efficiently. Our approach integrates seamlessly into existing RPA pipelines, automating resume parsing, job matching, and candidate notifications. Extensive performance benchmarking shows that MLAR outperforms the leading RPA platforms, including UiPath and Automation Anywhere, in high-volume resume-processing tasks. When processing 2,400 resumes, MLAR achieved an average processing time of 5.4 seconds per resume, reducing processing time by approximately 16.9% compared to Automation Anywhere and 17.1% compared to UiPath. These results highlight the potential of MLAR to transform recruitment workflows by providing an efficient, accurate, and scalable solution tailored to modern hiring needs.

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