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.

LLM4Jobs 是一种利用大型语言模型（LLMs）进行职位编码的新型无监督方法，通过在合成和实际数据集上进行严谨实验证明，LLM4Jobs 始终超过无监督最先进的基准，展示了其在不同数据集和细粒度上的多功能性。