From Lexical Baselines to Agentic Retrieval-Augmented Generation: Structured Skill and Responsibility-Level Extraction with the SFIA Framework
Bridging the gap between flat skill labels and professional competency levels through agentic RAG.
Automated skill extraction underpins workforce planning, yet most systems represent skills as flat labels with no notion of the responsibility level at which a skill is practiced. The Skills Framework for the Information Age (SFIA) captures exactly this dimension, defining 147 pr...
ArXiv CS.CL
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Integrating the SFIA framework into retrieval-augmented generation allows AI agents to map technical skills to specific professional responsibility levels. This transition from flat keyword matching to hierarchical competency modeling significantly improves the accuracy of automated workforce planning and talent allocation.
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