Constitutional Ai
Skill Verifiziert AktivAnthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
To enable the training of harmless AI models through AI-generated feedback and self-critique, reducing the need for human-labeled data and improving AI safety alignment.
Funktionen
- Implements Constitutional AI for AI safety training
- Details two-phase approach: Supervised Learning (SL) and RLAIF
- Provides Python code examples for self-critique, revision, and preference evaluation
- Addresses common issues and offers recovery strategies
Anwendungsfälle
- Safety alignment of LLMs without human labels
- Reducing harmful or toxic outputs from AI models
- Implementing explainable AI decisions through principles
- Scalable AI safety training using AI feedback
Nicht-Ziele
- Direct human preference data collection (RLHF)
- Runtime content filtering (NeMo Guardrails)
- Pre-trained moderation models (LlamaGuard)
Installation
Zuerst Marketplace hinzufügen
/plugin marketplace add Orchestra-Research/AI-Research-SKILLs/plugin install AI-Research-SKILLs@ai-research-skillsQualitätspunktzahl
VerifiziertVertrauenssignale
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95Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.
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