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The increased digitization of the labour market has given researchers,educators, and companies the means to analyze and better understand the labourmarket. However, labour market resources, although available in high volumes,tend to be unstructured, and as such, research towards methodologies for theidentification, linking, and extraction of entities becomes more and moreimportant. Against the backdrop of this quest for better labour marketrepresentations, resource constraints and the unavailability of large-scaleannotated data cause a reliance on human domain experts. We demonstrate theeffectiveness of prompt-based tuning of pre-trained language models (PLM) inlabour market specific applications. Our results indicate that cost-efficientmethods such as PTR and instruction tuning without exemplars can significantlyincrease the performance of PLMs on downstream labour market applicationswithout introducing additional model layers, manual annotations, and dataaugmentation.

Enhancing PLM Performance on Labour Market Tasks via Instruction-based Finetuning and Prompt-tuning with Rules