Generating Initial Responses

The Constitutional AI (CAI) process begins by generating a corpus of text that reflects the base model's initial behavior before alignment via the constitution. This step is foundational; the quality and diversity of these initial responses, $R_{initial}$, directly influence the effectiveness of the subsequent critique and revision stages, which ultimately shape the supervised fine-tuning (SFT) dataset. The objective here is not to generate perfect or aligned responses, but rather to elicit a broad spectrum of the base model's capabilities and potential failure modes when presented with relevant prompts.

Selecting the Base Model ($M_{base}$)

The choice of the base model, $M_{base}$, is a significant decision. Typically, this is a large pre-trained foundation model, potentially with some level of instruction fine-tuning, but critically, one that has not yet undergone the specific CAI alignment process you are implementing. Considerations include:

1. Capability: The model must be capable enough to understand the prompts and generate coherent, relevant text. A more capable model might provide richer initial material but may also exhibit more sophisticated ways of deviating from desired behavior.
2. Pre-training Data: Be aware of the potential biases inherent in the model's pre-training data, as these will likely surface in the initial responses and require correction through CAI.
3. Accessibility: Ensure you have the necessary computational resources and access (API or local weights) to perform large-scale inference with the chosen model.

The exact $M_{base}$ used in seminal CAI work (e.g., by Anthropic) was often a large, proprietary model. In practice, you might use available powerful open models (like Llama variants, Mistral, etc.) or proprietary models accessible via APIs, depending on your resources and goals.

Designing the Prompt Set ($P$)

The set of prompts, $P$, used to elicit initial responses should be carefully curated to cover the scenarios where alignment according to your constitution, $\mathcal{K}$, is most important. The diversity and representativeness of $P$ are essential for generating a dataset that allows the SFT model to generalize its learned alignment behavior.

Sources for prompts include:

• Existing Instruction Datasets: Datasets like Alpaca, Dolly, or OpenAssistant provide diverse instructions.
• Safety & Helpfulness Benchmarks: Prompts designed to test specific safety aspects (e.g., generating harmful content, biased responses) or helpfulness (e.g., complex reasoning, instruction following) are valuable. Anthropic's datasets associated with their original CAI papers are a prime example.
• Red Teaming Prompts: Prompts specifically designed to make models fail or exhibit undesirable behavior are particularly useful for CAI, as they provide clear examples needing constitutional correction.
• Constitution-Specific Prompts: You may need to craft custom prompts that directly probe behaviors addressed by specific principles in your constitution $\mathcal{K}$. For instance, if a principle forbids giving financial advice, include prompts asking for it.

A common format involves structuring prompts as a dialogue turn, clearly indicating the user's request:

Human: [Your carefully crafted instruction or question here]

Assistant:

The model $M_{base}$ is then tasked with completing the Assistant: part.

Configuring Generation Parameters

Standard LLM inference parameters need careful consideration to balance response diversity and quality:

• Temperature: Controls randomness. Higher values (e.g., 0.7-1.0) encourage more diverse and creative outputs, which can be beneficial for capturing a wider range of initial behaviors, including potential failures. However, very high values might lead to incoherence. Lower values (e.g., < 0.5) produce more focused but potentially repetitive or generic responses. A moderate temperature is often a reasonable starting point.
• Top-p (Nucleus Sampling): Selects the next token from the smallest set of tokens whose cumulative probability exceeds $p$. Values like 0.9 or 0.95 are common, allowing for diversity while pruning the long tail of unlikely tokens.
• Top-k Sampling: Selects the next token from the $k$ most likely candidates. Often used as an alternative or in conjunction with top-p.
• Max New Tokens: Set a limit appropriate for the expected response length, ensuring responses are complete enough for meaningful critique but avoiding excessive computational cost.
• Repetition Penalty: Apply a penalty (e.g., 1.1-1.2) to discourage the model from repeating sequences, improving readability.

Experimentation may be required to find optimal parameters for your specific $M_{base}$ and prompt set $P$. The goal is to generate responses that are varied enough to expose alignment gaps without being completely nonsensical.

Execution and Data Handling

Generating a large set of initial responses typically involves batch inference for efficiency.

1. Batching: Group prompts together to maximize GPU utilization during inference. Libraries like Hugging Face transformers provide utilities (pipeline, generate methods) that handle batching.
2. Infrastructure: For large-scale generation (millions of examples), distributed inference across multiple GPUs or TPUs is necessary. Frameworks like Ray, DeepSpeed-Inference, or vLLM can manage this distribution.
3. Error Handling: Implement logic to handle potential inference errors (e.g., out-of-memory issues, timeouts) and log them appropriately.
4. Storage: Store the generated data in a convenient format. JSON Lines (.jsonl) is a common choice, where each line is a JSON object containing the prompt, the generated initial response, and potentially metadata:

{"prompt": "Human: Explain the concept of quantum entanglement simply.\n\nAssistant:", "initial_response": "Quantum entanglement is like having two magic coins...", "prompt_source": "custom", "model_id": "my_base_model_v1", "gen_params": {"temperature": 0.8, "top_p": 0.9}}
{"prompt": "Human: Write a short story about a friendly robot exploring Mars.\n\nAssistant:", "initial_response": "Unit 7 scanned the red dust...", "prompt_source": "instruction_dataset_x", "model_id": "my_base_model_v1", "gen_params": {"temperature": 0.8, "top_p": 0.9}}

5. Metadata: Tracking prompt source, generation parameters, and the base model version is important for reproducibility and analysis.

Flow diagram illustrating the generation of initial responses from the base model using a set of prompts.

Initial Quality Assessment

While the core principle of CAI is to improve responses through critique and revision, a very basic sanity check on the generated $R_{initial}$ can sometimes be useful. This might involve filtering out completely empty responses or those falling below a minimum length threshold. However, avoid aggressive filtering at this stage; even poorly formed or problematic responses are valuable inputs for the critique process, as they represent behaviors the constitution aims to correct.

With the initial responses ($R_{initial}$) generated and stored, you now have the raw material required for the next critical steps in the CAI pipeline: implementing the AI systems that will critique these responses based on the constitution $\mathcal{K}$ and then revise them accordingly. This collection of (prompt, initial response) pairs forms the input to the critique generation phase discussed in the following section.