Job Skill Extraction via LLM-Centric Multi-Module Framework

• URL: http://arxiv.org/abs/2604.21525v1
• Date: Thu, 23 Apr 2026 10:46:07 GMT
• Title: Job Skill Extraction via LLM-Centric Multi-Module Framework
• Abstract summary: We present SRICL, an LLM-centric framework that combines semantic retrieval, in-context learning, and supervised fine-tuning.<n>On six public span-labeled corpora of job-ad sentences, SRICL achieves substantial STRICT-F1 improvements over GPT-3.5 prompting baselines and sharply reduces invalid tags and hallucinated spans.
• Score: 51.37063712967151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. SR pulls in-domain annotated sentences and definitions from ESCO to form format-constrained prompts that stabilize boundaries and handle coordination. SFT aligns output behavior, while the verifier enforces pairing, non-overlap, and BIO legality with minimal retries. On six public span-labeled corpora of job-ad sentences across sectors and languages, SRICL achieves substantial STRICT-F1 improvements over GPT-3.5 prompting baselines and sharply reduces invalid tags and hallucinated spans, enabling dependable sentence-level deployment in low-resource, multi-domain settings.

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