One-Round Active Learning through Data Utility Learning and Proxy Models
#labeled #labeling #annotators
The secret to making #AIChatbots sound #smart and #spew less #toxic nonsense is to use a technique called reinforcement learning from #HumanFeedback, which uses input from people to improve the model’s answers. It relies on a small army of #human #data #annotators who evaluate whether a string of text makes sense and sounds fluent and natural. They decide whether a response should be kept in the AI model’s database or removed. https://www.technologyreview.com/2023/06/13/1074560/we-are-all-ais-free-data-workers
#aichatbots #smart #spew #toxic #humanfeedback #human #data #annotators
Pareto Optimization for Active Learning under Out-of-Distribution Data Scenarios
#labeling #sampling #annotators