Optimizing Resume Design for ATS Compatibility: A Large Language Model Approach
Keywords:
Sematic, LLMs(Large Language Model), NLP(Natural Language Processing), Application, Tracking System (ATS)Abstract
The modern hiring process increasingly relies on Application Tracking Systems (ATS) to sort and evaluate resumes based on organizational preferences. However, many skilled and competent candidates fail to craft resumes that are ATS optimized, leading to missed opportunities. This study explores the potential of Large Language Models (LLMs) to enhance ATS compatibility in resumes. Using Natural Language Processing (NLP) techniques, LLMs analyze resumes to identify errors, recommend suitable keywords, enhance semantic alignment, and format content to meet ATS requirements. Evaluation results demonstrate that imple menting the model's suggested changes significantly improves ATS scores. This ap proach bridges the gap between job seekers and automated recruiting systems, empowering individuals to enhance their resumes effectively. By leveraging LLMs, job seekers gain a powerful tool to align their resumes with employer expectations, increasing their chances of success in the hiring process. This study highlights how LLMs can transform the recruitment landscape, improving access to employment and redefining traditional hiring practices
References
[1] Gan, C., Zhang, Q., & Mori, T. (2024). Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening. ArXiv, abs/2401.08315.
[2] Sunico, R.J., Pachchigar, S., Kumar, V., Shah, I., Wang, J., & Song, I. (2023). Resume Building Application based on LLM (Large Language Model). 2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 486-492.
[3] Du, Y., Luo, D., Yan, R., Liu, H., Song, Y., Zhu, H., & Zhang, J. (2023). Enhancing Job Recommendation through LLM-based Generative Adversarial Networks. ArXiv, abs/2307.10747.
[4] Çelik, D., Karakas, A., Bal, G., Gultunca, C., Elçi, A., Buluz, B., & Alevli, M.C. (2013). Towards an Information Extraction System Based on Ontology to Match Resumes and Jobs. 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops, 333-338.
[5] Thida, M. (2023). Automated Analysis of Job Market Demands using Large Language Model. International Journal of Advanced Computer Science and Applications.
[6] Skondras, P., Zervas, P., & Tzimas, G. (2023). Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification. Future Internet, 15, 363.
[7] Prathima, V. (2024). Resume Application Tracking System with Google Gemini Pro. International Journal for Research in Applied Science and Engineering Technology.
[8] Mohanty, S., Behera, A., Mishra, S., Alkhayyat, A., Gupta, D., & Sharma, V. (2023). Resumate: A Prototype to Enhance Recruitment Process with NLP based Resume Parsing. 2023 4th International Conference on Intelligent Engineering and Management (ICIEM), 1-6.
[9] D, A., S, K., R, N.E., K, K., S, J.M., & R, R. (2024). Resspar: AI-Driven Resume Parsing and Recruitment System using NLP and Generative AI. 2024 Second International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI), 1-6.
[10] Skondras, P., Zervas, P., & Tzimas, G. (2023). Generating Synthetic Resume Data with Large Language Models for Enhanced Job Description Classification. Future Internet, 15, 363.
