[learned]

A big caveat mentioned in the article is that this experiment was done with a small set (N=47) of specific questions that they expected to have relatively simple relational answers:

> The researchers developed a method to estimate these simple functions, and then computed functions for 47 different relations, such as “capital city of a country” and “lead singer of a band.” While there could be an infinite number of possible relations, the researchers chose to study this specific subset because they are representative of the kinds of facts that can be written in this way.

About 60% of these relations were retrieved using a linear function in the model. The remaining appeared to have nonlinear retrieval and is still a subject of investigation:

> Functions retrieved the correct information more than 60 percent of the time, showing that some information in a transformer is encoded and retrieved in this way. “But not everything is linearly encoded. For some facts, even though the model knows them and will predict text that is consistent with these facts, we can’t find linear functions for them. This suggests that the model is doing something more intricate to store that information,” he says.