#interpretability

Anthropic researchers have identified a 'global workspace' in large language models (LLMs), where information is integrated across layers, analogous to the Global Neuronal Workspace theory of consciousness. This finding provides a new framework for understanding how LLMs combine information across layers, potentially leading to improved model interpretability and alignment. The work defines a 'J-space' based on the expected change to final logits from perturbations at each layer, revealing a shared subspace across contexts.