HR Automation: Enhancing Candidate Onboarding Processes
Shital Pazare1, Vansh Kawa2, Riya Maurya3, Drashti Mehta4, Aryan Shah5
1 Artificial Intellegence and Data Science Department, Shah and Anchor Kutchhi Engineering College
2 Artificial Intellegence and Data Science Department, Shah and Anchor Kutchhi Engineering College
3 Artificial Intellegence and Data Science Department, Shah and Anchor Kutchhi Engineering College
4 Artificial Intellegence and Data Science Department, Shah and Anchor Kutchhi Engineering College
5 Artificial Intellegence and Data Science Department, Shah and Anchor Kutchhi Engineering College
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Abstract - Human Resources (HR) departments are faced with the problem of effectively managing a variety of activities, from talent acquisition to legal compliance, in the dynamic workforce landscape of today. This study explores the ground breaking integration of Artificial Intelligence (AI), with an emphasis on Natural Language Processing (NLP), as a transformative force in reimagining Human Resources (HR) management methodologies."
The pivotal role of two cutting-edge AI tools, the Legal Language Model (LLM) and the Language Model for Attribute-based Matching (LAMA), is examined in addressing key HR challenges. LLM employs sophisticated NLP techniques to automate and refine the interpretation of complex legal documents, ensuring compliance and reducing legal risks. Concurrently, LAMA transforms the recruitment paradigm by facilitating a nuanced, attribute-based alignment of candidates' profiles with job requirements, thereby optimizing the talent acquisition process.
We explore the integration of these models within an interactive framework provided by Streamlit, a Python tool for creating user-engaging web applications. This integration offers HR professionals an intuitive platform for accessing, visualizing, and interacting with model-generated insights, fostering informed decision-making and enhanced operational efficiency. Our analysis reveals significant improvements in the precision of recruitment processes, efficacy in legal document handling, and overall workflow streamlining. The findings advocate for the adoption of these AI-driven tools in HR, highlighting their potential to revolutionize traditional practices and contribute to the strategic growth of organizations in the contemporary digital landscape.
Key Words: Human Resources Management, Legal Language Model (LLM), Language Model for Attribute-based Matching (LAMA), Streamlit, HR Technology Integration, HR Automation.
