Language models can explain neurons in language models

Language models like GPT-3 are composed of many individual neurons, each of which is responsible for processing a particular aspect of the input text. These neurons can be thought of as individual processing units that work together to produce the model's output.

While it is difficult to explain the exact function of each neuron in a language model, there are techniques that can be used to gain some insight into their behavior. One such technique is activation maximization, which involves finding the input text that maximally activates a particular neuron.

By examining the input text that maximally activates a given neuron, we can gain some insight into the type of text that the neuron is sensitive to. For example, a neuron that is maximally activated by words related to food might be involved in processing information about food.

In this way, language models can provide a window into the inner workings of their individual neurons, allowing us to gain some insight into the mechanisms underlying their impressive performance on a wide range of language tasks.