Hugging Face paper explores three models for RLHF annotation

A new paper proposes three distinct models for understanding the role of human annotators in Reinforcement Learning from Human Feedback (RLHF) pipelines. These models are 'extension,' where annotators mirror designers' judgments; 'evidence,' where annotators provide factual information; and 'authority,' where annotators represent broader population views. The paper argues that clarifying which model is used for different annotation tasks can improve RLHF pipeline design and aggregation methods. AI

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IMPACT Clarifies annotation models for RLHF, potentially improving alignment and safety.

RANK_REASON Academic paper analyzing RLHF annotation methods.

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• Reinforcement Learning with Human Feedback
• RLHF

• paper
• safety