Constitutional AI: Harmlessness from AI Feedback (2212.08073v1)

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.

• The paper introduces Constitutional AI, a two-phase training method that uses constitutional principles to self-regulate and curb harmful outputs.
• It employs a supervised learning phase coupled with reinforcement learning from AI feedback to iteratively refine model behavior.
• Experimental results demonstrate that this approach achieves higher harmlessness metrics than traditional human-guided reinforcement methods.

Constitutional AI: Harmlessness from AI Feedback

Introduction

"Constitutional AI: Harmlessness from AI Feedback" introduces a novel approach for developing AI systems that maintain helpfulness, honesty, and harmlessness, particularly as AI capabilities progressively reach or surpass human levels. The authors propose a method named Constitutional AI (CAI), which aims to self-improve AI behavior with minimal human oversight. Here, human feedback and the associated resource costs are minimized by relying on a set of formally defined principles, termed a 'constitution.' The core idea is to enable AI systems to supervise other AIs, further automating the robustness verification process. This paper is structured around two primary training phases: a supervised learning stage and reinforcement learning from AI feedback (RLAIF) stage.

Methodology

The CAI approach comprises two phases:

1. Supervised Learning (SL) Phase:
   • The authors leverage sample responses from an initial AI model subjected to critiques and revisions guided by constitutional principles.
   • An AI system evaluates its own responses, identifies harmful aspects, and generates revised responses which are more aligned with the constitution.
   • These steps are repeated iteratively for enhanced refinement.
   • The resulting dataset of revised responses is used to finetune the initial model, incrementally improving its behavior by addressing harmfulness without increased evasion.
2. Reinforcement Learning (RL) Phase:
   • The foundation model, refined in the SL phase, is further improved using RL where the reward signals are directly derived from AI feedback (RLAIF).
   • Model-generated critiques and evaluations create a dataset representing AI preferences, eliminating the need for human labels.
   • The model undergoes RL using these preferences as reward signals to enhance its performance further while ensuring its behavior remains within the constitutional bounds.

Results

Evaluation and Performance

The paper presents several experimental results evaluating the efficacy of the CAI approach:

• Humanless Harmfulness Identification:
  • The AI model demonstrates significant promise in identifying harmful behaviors, achieving progressively closer performance to human feedback models.
• Improvement Through Revisions:
  • Iterative revisions using self-critiques effectively reduced harmfulness, as observed by corresponding increases in preference model (PM) scores.
  • Numerical results show improved harmlessness and helpfulness metrics for models trained with CAI methods compared to traditional RLHF models, particularly at higher model scales.

Strong Numerical Results

• The SL-CAI models consistently showed better harmlessness scores compared to purely helpful RLHF models.
• Noteworthy improvement was illustrated by the combination of SL and RL phases (RL-CAI), allowing models to excel in performing more harmless without being evasive.

Implications and Future Directions

Practical Implications

The shift towards AI-generated feedback mechanisms changes the landscape of AI alignment. By reducing dependency on exhaustive human label collection, the CAI method enhances transparency and efficiency in supervising AI behavior. This approach also opens avenues for scaling supervision as AI capabilities advance, ensuring continuous alignment of increasingly proficient models.

Theoretical Implications

From a theoretical standpoint, the introduction of chain-of-thought (CoT) reasoning for evaluating AI decisions marks a significant step. Detailed self-critiques embedded in AI learning loops demonstrate a path forward for making AI processes more interpretable and trustworthy. Additionally, employing simplified constitutional principles allows researchers to codify ethical guidelines that directly shape AI behaviors, fostering better control over generalization patterns.

Future Developments

The potential future enhancements involve:

• Applying CAI principles to different dimensions of AI behavior, including modifying tone, style, or specialized task responses.
• Scaling red teaming practices through automated AI supervision, enhancing the robustness and diversity of dataset distributions.
• Iterated "online" training with continual preference model updates to mirror current policy distributions dynamically.

Conclusion

The proposed Constitutional AI approach successfully introduces a framework to cultivate AI systems that are not only more harmless but also transparent and ethically grounded, relying substantially less on direct human intervention. The results affirm the model's robustness and non-evasive handling of harmful queries, marking a considerable step in advancing AI alignment. Future explorations will further refine these methods, potentially revolutionizing how AI oversight and behavioral training are conducted.