#llm/training

Claude’s constitution is the foundational document that both expresses and shapes who Claude is. It contains detailed explanations of the values we would like Claude to embody and the reasons why. In it, we explain what we think it means for Claude to be helpful while remaining broadly safe, ethical, and compliant with our guidelines. The constitution gives Claude information about its situation and offers advice for how to deal with difficult situations and tradeoffs, like balancing honesty with compassion and the protection of sensitive information. Although it might sound surprising, the constitution is written primarily for Claude. It is intended to give Claude the knowledge and understanding it needs to act well in the world.

Claude itself also uses the constitution to construct many kinds of synthetic training data, including data that helps it learn and understand the constitution, conversations where the constitution might be relevant, responses that are in line with its values, and rankings of possible responses. All of these can be used to train future versions of Claude to become the kind of entity the constitution describes. This practical function has shaped how we’ve written the constitution: it needs to work both as a statement of abstract ideals and a useful artifact for training.

In the first stage of model training, pre-training, LLMs are asked to read vast amounts of text. Through this, they learn to simulate heroes, villains, philosophers, programmers, and just about every other character archetype under the sun. In the next stage, post-training, we select one particular character from this enormous cast and place it center stage: the Assistant. It’s in this character that most modern language models interact with users.

But who exactly is this Assistant? Perhaps surprisingly, even those of us shaping it don't fully know. We can try to instill certain values in the Assistant, but its personality is ultimately shaped by countless associations latent in training data beyond our direct control. What traits does the model associate with the Assistant? Which character archetypes is it using for inspiration? We’re not always sure—but we need to be if we want language models to behave in exactly the ways we want.

In a new paper, conducted through the MATS and Anthropic Fellows programs, we look at several open-weights language models, map out how their neural activity defines a “persona space,” and situate the Assistant persona within that space.

We find that Assistant-like behavior is linked to a pattern of neural activity that corresponds to one particular direction in this space—the “Assistant Axis”—that is closely associated with helpful, professional human archetypes. By monitoring models’ activity along this axis, we can detect when they begin to drift away from the Assistant and toward another character. And by constraining their neural activity (“activation capping”) to prevent this drift, we can stabilize model behavior in situations that would otherwise lead to harmful outputs.

The Assistant Axis (defined as the mean difference in activations between the Assistant and other personas) aligns with the primary axis of variation in persona space. This occurs across different models