Transformer language models (LMs) have been shown to represent concepts as
directions in the latent space of hidden activations. However, for any given
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 at a given
hidden layer in a transformer LM by first modeling the relation between subject
and object as a linear relational embedding (LRE). While the LRE work was
mainly presented as an exercise in understanding model representations, we find
that inverting the LRE while using earlier object layers results in a powerful
technique to find concept directions that both work well as a classifier and
causally influence model outputs.

用于在 Transformer 语言模型的给定隐藏层中找到对应于可解释的人类概念的概念方向的线性关系概念技术（LRC）通过首先将主体和客体之间的关系建模为线性关系嵌入（LRE），并在倒转 LRE 同时使用较早的客体层，从而找到既可以作为分类器良好工作又能因果地影响模型输出的概念方向。