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 striking 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
taking over abruptly in the later half. This internal phase shift manifests in
different functional components: attention heads that write the answer token
predominantly appear in the later half, attention heads that move information
along ontological relationships appear exclusively 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.

通过对大型语言模型的内部机制进行机械性探索，我们发现大型语言模型在连续思考生成上部署多个并行路径，产生了顺序答案，并观察到模型中的功能分层差异，这是对连续思考推理进行机械性研究的首次尝试。