  Transformer language models (LMs) have been shown to represent concepts as
directions in the latent space of hidden activations. However, for any
human-interpretable concept, how can we find its direction in the latent space?
We present a technique called linear relational concepts (LRC) for finding
concept directions corresponding to human-interpretable concepts by first
modeling the relation between subject and object as a linear relational
embedding (LRE). We find that inverting the LRE and using earlier object layers
results in a powerful technique for finding concept directions that outperforms
standard black-box probing classifiers. We evaluate LRCs on their performance
as concept classifiers as well as their ability to causally change model
output.
