Personalised soft prompt tuning in pre-trained language models: Bridging multitask transfer learning and crowdsourcing learning

Abstract

Soft prompt tuning significantly enhances the performance of pre-trained language models, especially in complex tasks where abundant annotated data is available. Crowdsourcing provides a cost-effective means of obtaining large-scale annotations; however, it can hinder the effectiveness of soft prompt tuning due to varying annotation criteria among different annotators, introducing data noise and degrading performance. To address this issue, we conceptualise annotations from each annotator as a subtask and frame crowdsourcing learning as multitask transfer learning. We propose a novel soft prompt tuning method utilising personalised prompts designed to capture the principles of individual annotators through a knowledge distillation approach. To validate our hypothesis, we apply our method across four benchmark datasets in two specific crowdsourcing tasks: crowdsourced named entity recognition (CNER) and crowdsourced relation extraction (CRE). Our personalised soft prompt method shows significant improvements, with average increases of 8.96% in CNER and 14.44% in CRE compared to the standard soft prompt tuning method, while also achieving competitive results against state-of-the-art crowdsourcing methods.