{
  "id": "anthropic-sitemap:research:constitutional-ai-harmlessness-from-ai-feedback",
  "type": "article",
  "title": "Constitutional AI: Harmlessness from AI Feedback",
  "abstract": "As AI systems become more capable, we would like to enlist their help to supervise other AIs. We experiment with methods for training a harmless AI assistant through self-improvement, without any human labels identifying harmful outputs. The only human oversight is provided through a list of rules or principles, and so we refer to the method as 'Constitutional AI'. The process involves both a supervised learning and a reinforcement learning phase. In the supervised phase we sample from an initial model, then generate self-critiques and revisions, and then finetune the original model on revised responses. In the RL phase, we sample from the finetuned model, use a model to evaluate which of the two samples is better, and then train a preference model from this dataset of AI preferences. We then train with RL using the preference model as the reward signal, i.e. we use 'RL from AI Feedback' (RLAIF). As a result we are able to train a harmless but non-evasive AI assistant that engages with harmful queries by explaining its objections to them. Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the human-judged performance and transparency of AI decision making. These methods make it possible to control AI behavior more precisely and with far fewer human labels.",
  "issued": {
    "date-parts": [
      [
        2022,
        12,
        15
      ]
    ]
  },
  "URL": "https://www.anthropic.com/research/constitutional-ai-harmlessness-from-ai-feedback",
  "publisher": "Anthropic",
  "source": "vendor/anthropic-sitemap/research/constitutional-ai-harmlessness-from-ai-feedback.md"
}