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Understanding labour market dynamics requires accurately identifying theskills required for and possessed by the workforce. Automation techniques areincreasingly being developed to support this effort. However, automaticallyextracting skills from job postings is challenging due to the vast number ofexisting skills. The ESCO (European Skills, Competences, Qualifications andOccupations) framework provides a useful reference, listing over 13,000individual skills. However, skills extraction remains difficult and accuratelymatching job posts to the ESCO taxonomy is an open problem. In this work, wepropose an end-to-end zero-shot system for skills extraction from jobdescriptions based on large language models (LLMs). We generate synthetictraining data for the entirety of ESCO skills and train a classifier to extractskill mentions from job posts. We also employ a similarity retriever togenerate skill candidates which are then re-ranked using a second LLM. Usingsynthetic data achieves an RP@10 score 10 points higher than previous distantsupervision approaches. Adding GPT-4 re-ranking improves RP@10 by over 22points over previous methods. We also show that Framing the task as mockprogramming when prompting the LLM can lead to better performance than naturallanguage prompts, especially with weaker LLMs. We demonstrate the potential ofintegrating large language models at both ends of skills matching pipelines.Our approach requires no human annotations and achieve extremely promisingresults on skills extraction against ESCO.

Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers