RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts

• URL: http://arxiv.org/abs/2406.06577v1
• Date: Tue, 4 Jun 2024 08:34:19 GMT
• Title: RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts
• Authors: Jing Yang, Xiao Wang, Yu Zhao, Yuhang Liu, Fei-Yue Wang,
• Abstract summary: We propose a retrieval-augmented generation-based crowdsourcing framework that reimagines task decomposition (TD) as event detection from the perspective of natural language understanding. We present a Prompt-Based Contrastive learning framework for TD (PBCT), which incorporates a prompt-based trigger detector to overcome dependence. Experiment results demonstrate the competitiveness of our method in both supervised and zero-shot detection.
• Score: 21.69333828191263
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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, pre-trained language models (PLMs)-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 reimagines 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 introduced to provide varying attention to trigger and contextual features according to different event types. Experiment results demonstrate the competitiveness of our method in both supervised and zero-shot detection. A case study on printed circuit board manufacturing is showcased to validate its adaptability to unknown professional domains.

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