  Relational concepts are indeed foundational to the structure of knowledge
representation, as they facilitate the association between various entity
concepts, allowing us to express and comprehend complex world knowledge. By
expressing relational concepts in natural language prompts, people can
effortlessly interact with large language models (LLMs) and recall desired
factual knowledge. However, the process of knowledge recall lacks
interpretability, and representations of relational concepts within LLMs remain
unknown to us. In this paper, we identify hidden states that can express entity
and relational concepts through causal mediation analysis in fact recall
processes. Our finding reveals that at the last token position of the input
prompt, there are hidden states that solely express the causal effects of
relational concepts. Based on this finding, we assume that these hidden states
can be treated as relational representations and we can successfully extract
them from LLMs. The experimental results demonstrate high credibility of the
relational representations: they can be flexibly transplanted into other fact
recall processes, and can also be used as robust entity connectors. Moreover,
we also show that the relational representations exhibit significant potential
for controllable fact recall through relation rewriting.
