As language models continue to grow in capability and deployment, understanding their internal workings remains a challenge. Interpretability research aims to analyze the individual components, such as neurons and attention heads, to gain better insight into model behavior. However, traditional manual inspection of neurons is difficult to scale for larger neural networks with billions of parameters.

To address this issue, researchers propose an automated process using GPT-4 to provide and score natural language explanations of neuron behavior in other language models. This approach is part of efforts to automate alignment research and has the advantage of scaling with the progress of AI development. As models become more intelligent, the explanations provided will also improve, leading to a better understanding of AI systems.