  Large language models (LLMs) have shown an impressive ability to perform
tasks believed to require thought processes. When the model does not document
an explicit thought process, it becomes difficult to understand the processes
occurring within its hidden layers and to determine if these processes can be
referred to as reasoning. We introduce a novel and interpretable analysis of
internal multi-hop reasoning processes in LLMs. We demonstrate that the
prediction process for compositional reasoning questions can be modeled using a
simple linear transformation between two semantic category spaces. We show that
during inference, the middle layers of the network generate highly
interpretable embeddings that represent a set of potential intermediate answers
for the multi-hop question. We use statistical analyses to show that a
corresponding subset of tokens is activated in the model's output, implying the
existence of parallel reasoning paths. These observations hold true even when
the model lacks the necessary knowledge to solve the task. Our findings can
help uncover the strategies that LLMs use to solve reasoning tasks, offering
insights into the types of thought processes that can emerge from artificial
intelligence. Finally, we also discuss the implication of cognitive modeling of
these results.
