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Once you've requested control tasks, the platform's algorithms analyze your pool and select the tasks that are best suited for quality control. These tasks are then labeled by experienced performers and uploaded back to your pool. Launching a pool that contains control tasks allows you to closely monitor percentages of correct answers. This way, you can block any Toloker whose work you aren't satisfied with, leading to an improved dataset quality overall.
A few things to keep in mind:
If you have a different type of project and can't see this option but would like to use it, please contact us on Slack or write to our Support team.
With control tasks in your pool, you can track how well performers are doing on your project and use that information to improve labeling quality. The important thing to remember is that control tasks alone don't affect quality. To see actual improvements, you need to set up quality control rules. For example, you can set up a rule to block Tolokers who perform poorly, or grant/revoke access to tasks based on their skill level. You can also use this information to reward diligent performers, offering them an additional incentive to keep up the good work. Learn more about control tasks and quality control rules here.
Get startedToloka values your opinion - we work 24/7 to offer you the highest possible labeling quality. Please share your feedback about this new feature and help us improve our product. You can reach us on Slack or by emailing our Support team.