Annotation Simplicity in RLHF: Recognition over Demonstration
The preference-based annotation in RLHF is simpler than the demonstration required for supervised learning because human values are often hard to articulate and even more challenging to exemplify perfectly. Instead of asking annotators to create ideal responses, RLHF presents them with a list of model-generated outputs and asks them to rank their preferences. This method is highly effective in situations where desired behaviors are difficult to demonstrate but can be easily recognized, shifting the annotation task from content creation to evaluation.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Annotation Simplicity in RLHF: Recognition over Demonstration
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