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

翻译：劳动力市场数字化程度的提高，使研究人员、教育工作者和企业能够分析并更好地理解劳动力市场。然而，劳动力市场资源虽然数量庞大，但往往是非结构化的。因此，针对实体识别、链接和提取的方法研究变得越来越重要。在寻求更好的劳动力市场表征的背景下，资源限制和缺乏大规模标注数据使得依赖于人类领域专家。我们证明了基于提示的预训练语言模型（PLM）调优在劳动力市场特定应用中的有效性。我们的结果表明，像PTR和无样本指令微调这样成本高效的方法，可以在不引入额外模型层、人工标注和数据增强的情况下，显著提升PLM在下游劳动力市场应用中的性能。