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LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
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LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
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Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation. This paper introduces LLM4Jobs, a novel unsupervised methodology that taps into the capabilities of large language models (LLMs) for occupation coding. LLM4Jobs uniquely harnesses both the natural language understanding and generation capacities of LLMs. Evaluated on rigorous experimentation on synthetic and real-world datasets, we demonstrate that LLM4Jobs consistently surpasses unsupervised state-of-the-art benchmarks, demonstrating its versatility across diverse datasets and granularities. As a side result of our work, we present both synthetic and real-world datasets, which may be instrumental for subsequent research in this domain. Overall, this investigation highlights the promise of contemporary LLMs for the intricate task of occupation extraction and standardization, laying the foundation for a robust and adaptable framework relevant to both research and industrial contexts.
Forward citations
Cited by 5 Pith papers
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STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling
STEP, with ROUTE embeddings and JobHop v2, sets new next-job prediction SOTA on four ESCO career-trajectory benchmarks by modeling inter-job time and education.
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JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes
JobHop v2 releases 355,315 ESCO-annotated career trajectories with temporal and education fields, extracted by a reasoning-controlled LLM pipeline from real VDAB resumes at near inter-annotator quality.
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STEP: Career-Path Recommendation via Temporal and Educational Trajectory Modeling
A time- and education-aware GRU recommender beats prior baselines on next-job prediction across four career-trajectory benchmarks, though most of the gain comes from a learnable temperature parameter.
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JobHop v2: A Large-Scale Career Trajectory Dataset from Unstructured Resumes
JobHop v2 is a public dataset of 355,315 LLM-extracted career trajectories from multilingual resumes, with ESCO codes, quarterly dates, and five education levels.
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Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn
A GPT-4-distilled small LM plus grouped LoRA adapters improves LinkedIn's job-attribute classification over legacy models.
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