AI RESEARCH
Job Skill Extraction via LLM-Centric Multi-Module Framework
arXiv CS.CL
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ArXi:2604.21525v1 Announce Type: new Span-level skill extraction from job advertisements underpins candidate-job matching and labor-market analytics, yet generative large language models (LLMs) often yield malformed spans, boundary drift, and hallucinations, especially with long-tail terms and cross-domain shift. We present SRICL, an LLM-centric framework that combines semantic retrieval (SR), in-context learning (ICL), and supervised fine-tuning (SFT) with a deterministic verifier.