RAG-Based Crowdsourcing Task Decomposition via Masked Contrastive Learning With Prompts

Crowdsourcing is a critical technology in social manufacturing, which leverages an extensive and boundless reservoir of human resources to handle a wide array of complex tasks. The successful execution of these complex tasks relies on task decomposition (TD) and allocation, with the former being a prerequisite for the latter. Recently, pretrained language models-based methods have garnered significant attention. However, they are constrained to handling straightforward common-sense tasks due to their inherent restrictions involving limited and difficult-to-update knowledge as well as the presence of "hallucinations." To address these issues, we propose a retrieval-augmented generation-based crowdsourcing framework that reformulates TD as event detection from the perspective of natural language understanding. However, the existing detection methods fail to distinguish differences between event types and always depend on heuristic rules and external semantic analyzing tools. Therefore, we present a prompt-based contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Additionally, trigger-attentive sentinel and masked contrastive learning are designed to provide varying attention to trigger and contextual features according to different event types. Extensive experiment results demonstrate our method is highly competitive in both supervised and zero-shot detection. A case study on printed circuit board design and manufacturing is used to validate its adaptability and scalability in unfamiliar professional domains.