Summary of Rag-based Crowdsourcing Task Decomposition Via Masked Contrastive Learning with Prompts, by Jing Yang et al.
RAG-based Crowdsourcing Task Decomposition via Masked Contrastive Learning with Prompts
by Jing Yang, Xiao Wang, Yu Zhao, Yuhang Liu, Fei-Yue Wang
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel crowdsourcing framework, leveraging pre-trained language models (PLMs), is proposed to tackle complex tasks. The framework reimagines task decomposition (TD) as event detection from a natural language understanding perspective. Existing TD methods rely on heuristic rules and external tools, which are limitations our Prompt-Based Contrastive learning framework (PBCT) addresses. PBCT incorporates a prompt-based trigger detector, trigger-attentive sentinel, and masked contrastive learning to provide varying attention to trigger and contextual features according to different event types. Experimental results demonstrate the competitiveness of our method in both supervised and zero-shot detection. A case study on printed circuit board manufacturing showcases its adaptability to unknown professional domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a new way for people to work together online, making complex tasks easier. This new approach uses special computer models that understand language. It breaks down big tasks into smaller ones and decides who does what. Right now, these models are good at simple tasks but struggle with harder ones. To fix this, we created a new system that helps the models better understand what’s happening. We tested it on a real-world project, making printed circuit boards, and showed it can work well even in unknown situations. |
Keywords
» Artificial intelligence » Attention » Event detection » Language understanding » Prompt » Supervised » Zero shot
