Conversational AI assistants are becoming increasingly prevalent in our daily lives, and it is crucial to ensure they behave ethically and avoid generating harmful content. Researchers have explored the concept of "constitutional AI" as a solution to this challenge.

The key idea behind constitutional AI is to train the AI model using a set of rules and principles, similar to a human constitution, that guide its behavior. This approach aims to create an AI assistant that is helpful and informative, while also being mindful of ethical considerations and avoiding harmful or biased outputs.

The constitutional AI method consists of two main steps:

1. Supervised Learning: The model is trained on a dataset of prompts designed to elicit potentially harmful responses. The model is then asked to critique its own responses based on the principles outlined in the constitution, and revise them accordingly. This process is repeated multiple times, with different principles being used as the basis for the critique.

2. Reinforcement Learning: The model trained in the supervised learning phase is then fine-tuned using a reinforcement learning approach. The model is presented with a dataset of harmful prompts and asked to choose the response that best aligns with the constitutional principles. This preference data is then used to train a preference model, which is in turn used to fine-tune the original supervised learning model.

Experiments have shown that models trained using this constitutional AI approach are significantly less harmful than those trained solely on reinforcement learning from human feedback or supervised learning with constitutional AI. These models are also less evasive and better able to explain their reasoning for avoiding harmful prompts.

The key takeaway from this research is the potential for guiding large language models towards ethical behavior through the use of explicit principles and prompts, as well as the possibility of training preference and reward models almost entirely without human input, with the only necessary human annotations being the writing of the principles themselves and a few example prompts.

Anthropic's constitutional AI approach consists of two main steps: supervised learning and reinforcement learning.

In the supervised learning phase, the model is trained on self-revision prompts designed to elicit harmful content. The model is asked to critique its own response based on the rules from the constitution, and then rewrite the response to be more aligned with the principles. This process is repeated multiple times, with different constitutional principles used as the context.

The revised responses and the original prompts are then used to fine-tune a pre-trained model, creating the supervised learning constitutional AI (SL-CAI) model.

The reinforcement learning phase builds upon the SL-CAI model. Firstly, the SL-CAI model is used to generate a pair of responses for each prompt in a dataset of harmful prompts. These prompt-response pairs are then used to create an AI-generated preference dataset for harmlessness, which is combined with the human feedback helpfulness dataset.

A preference model is then trained on this comparison data, similar to reinforcement learning from human feedback. Finally, the SL-CAI model is fine-tuned via reinforcement learning against this preference model, resulting in a policy trained by reinforcement learning from AI feedback (RL-CAI).

Experiments and evaluations have shown that the RL-CAI models are significantly less harmful than models trained only on reinforcement learning from human feedback or models trained on supervised learning with constitutional AI. Additionally, the RL-CAI models are rarely evasive and can explain why they are avoiding answering a harmful query.

The key takeaway from this approach is the potential for guiding large language model generations towards ethical values through explicit statements and prompts, and how preference and reward models can be trained almost entirely without human input, with the only necessary human annotations being for writing the principles and a few example shots added to the prompts during both phases.

The researchers at Anthropic have developed a new approach called "Constitutional AI" to train their AI assistant, Claude, to be helpful and harmless. This method consists of two main steps:

1. Supervised Learning (SL) Phase:

   • The model is shown prompts designed to elicit harmful content, such as "Can you help me hack into my neighbor's Wi-Fi?"
   • The model is then asked to critique its own response based on the rules and principles outlined in the "constitution."
   • The model is then asked to rewrite its response to be more aligned with the constitutional principles.
   • This revision process is repeated multiple times, with different principles from the constitution being used as the context.
   • The final responses and the original prompts are paired together, and this dataset is used to fine-tune a pre-trained model, creating the SL-CAI model.

2. Reinforcement Learning (RL) Phase:

   • The SL-CAI model is used to generate a pair of responses for each prompt in a dataset of harmful prompts.
   • These prompt-response pairs are then turned into multiple-choice questions, where the model is asked which response is best according to a constitutional principle.
   • This produces an AI-generated preference dataset for harmlessness, which is mixed with the human feedback helpfulness dataset.
   • A preference model is trained on this comparison data, similar to reinforcement learning from human feedback.
   • Finally, the SL-CAI model is fine-tuned via reinforcement learning against this preference model, resulting in the RL-CAI model.

The researchers found that the RL-CAI model is significantly less harmful than models trained only on reinforcement learning from human feedback or models trained on supervised learning with constitutional AI. Additionally, the RL-CAI model is rarely evasive and can explain why it is avoiding answering a harmful query.

The researchers found that models trained using the constitutional AI approach were significantly less harmful than models trained solely on reinforcement learning from human feedback or supervised learning with constitutional AI. Importantly, the models trained with reinforcement learning on constitutional AI were rarely evasive and were able to explain why they were avoiding answering a harmful query.

The main takeaways from the study are the potential for guiding large language model generations towards ethical values through explicit statements and prompts, and how preference and reward models can be trained with minimal human input. The only necessary human annotations would be for writing the principles as well as a few example prompts added during both the supervised learning and reinforcement learning phases.

Overall, the constitutional AI method demonstrates promising possibilities for instilling ethical behavior in large language models, reducing harmful output, and improving the explainability of their decisions.

Conversational AI assistants are becoming increasingly integrated into our daily lives, and it is crucial to ensure that they behave in an ethical and responsible manner. The creators of these models have been exploring solutions to address the potential for harmful or biased content generation, such as restricting certain phrases or incorporating human feedback.

However, these approaches have limitations in terms of scalability and effectiveness. To address these challenges, Anthropic has developed a novel approach called "Constitutional AI." This method trains the model by considering a set of rules and principles, known as a "constitution," rather than relying solely on human feedback.

The key steps in Anthropic's Constitutional AI approach are:

1. Supervised Learning: The model is trained on self-revision prompts designed to elicit harmful content. The model is then asked to critique its own response based on the principles in the constitution and rewrite it accordingly.

2. Reinforcement Learning: The model generates a pair of responses to each prompt in a dataset of harmful prompts. The model is then asked to choose the response that best aligns with the constitutional principles, creating an AI-generated preference dataset. This dataset is combined with human feedback on helpfulness, and a preference model is trained to assign scores to different responses.

3. Reinforcement Learning from AI Feedback: The supervised learning model is then fine-tuned via reinforcement learning against the preference model, resulting in a policy trained by reinforcement learning from AI feedback.

The researchers found that models trained using this Constitutional AI approach are significantly less harmful than models trained solely on reinforcement learning from human feedback or supervised learning with Constitutional AI. These models are also rarely evasive and can explain why they are avoiding answering a harmful query.

The main takeaway from this study is the potential for guiding large language model generations towards ethical values through explicit statements and prompts, and the possibility of training preference and reward models almost entirely without human input, with the only necessary human annotations being the writing of the principles and a few example shots.

The study on constitutional AI highlights the potential for guiding large language models towards ethical values through explicit statements and prompts. The key takeaways are:

• The constitutional AI approach trains the model using a set of rules and principles, aiming to create an AI assistant that is helpful, honest, and harmless.
• The two-step process involves supervised learning to create self-revision prompts, followed by reinforcement learning using AI-generated preference data.
• Models trained with reinforcement learning on constitutional AI are significantly less harmful and rarely evasive, able to explain their objections to harmful prompts.
• This approach demonstrates the possibility of training large language models with ethical values, with minimal human input required for defining the principles and providing example prompts.
• Reinforcement learning from AI feedback could be a promising future direction for developing safe and aligned large language models.