Summary of Estimating Contribution Quality in Online Deliberations Using a Large Language Model, by Lodewijk Gelauff et al.
Estimating Contribution Quality in Online Deliberations Using a Large Language Model
by Lodewijk Gelauff, Mohak Goyal, Bhargav Dindukurthi, Ashish Goel, Alice Siu
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 This paper presents an AI-powered platform for facilitating large-scale online discussions, specifically designed for deliberation. The Stanford Online Deliberation Platform leverages video-based discussions on a structured agenda to enable small-group interactions without requiring human moderators. The platform’s effectiveness is demonstrated through various deliberation events, including a global event conducted with Meta across 32 countries and another with US post-secondary institutions. The paper showcases the potential of this platform in addressing polarization by facilitating knowledge sharing, argumentation, and perspective-taking among participants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research created a special online space where people can talk about important issues together. They call it the Stanford Online Deliberation Platform. It’s like a big video chat room where small groups discuss specific topics without needing someone to lead the conversation. The platform was used in many events, including one that involved people from 32 countries working with Meta and another with US colleges. This helps show how this tool can bring people together and help them understand each other’s perspectives. |
