Summary of Adaptive Crowdsourcing Via Self-supervised Learning, by Anmol Kagrecha et al.
Adaptive Crowdsourcing Via Self-Supervised Learning
by Anmol Kagrecha, Henrik Marklund, Benjamin Van Roy, Hong Jun Jeon, Richard Zeckhauser
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In a novel approach to crowdsourcing, researchers introduce “predict-each-worker,” a self-supervised learning-based method that adapts weights assigned to individual contributors based on their previous estimates. This approach is shown to outperform traditional averaging methods when skills vary among crowdworkers or their estimates correlate. The new algorithm accommodates complex models and practical challenges, making it a promising solution for large-scale crowdsourcing applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to combine the ideas of many people on the internet to get an accurate answer. They call this method “predict-each-worker.” It’s like a special kind of math that takes into account how good each person is at guessing and uses that information to make the final answer more accurate. This can be really helpful when there are lots of different opinions or when some people are better than others at getting the right answer. |
Keywords
* Artificial intelligence * Self supervised
