  Despite superior reasoning prowess demonstrated by Large Language Models
(LLMs) with Chain-of-Thought (CoT) prompting, a lack of understanding prevails
around the internal mechanisms of the models that facilitate CoT generation.
This work investigates the neural sub-structures within LLMs that manifest CoT
reasoning from a mechanistic point of view. From an analysis of Llama-2 7B
applied to multistep reasoning over fictional ontologies, we demonstrate that
LLMs deploy multiple parallel pathways of answer generation for step-by-step
reasoning. These parallel pathways provide sequential answers from the input
question context as well as the generated CoT. We observe a functional rift in
the middle layers of the LLM. Token representations in the initial half remain
strongly biased towards the pretraining prior, with the in-context prior taking
over in the later half. This internal phase shift manifests in different
functional components: attention heads that write the answer token appear in
the later half, attention heads that move information along ontological
relationships appear in the initial half, and so on. To the best of our
knowledge, this is the first attempt towards mechanistic investigation of CoT
reasoning in LLMs.
