Towards a Mechanistic Interpretation of Multi-Step Reasoning
  Capabilities of Language Models
Yifan Hou Jiaoda Li Yu Fei Alessandro Stolfo Wangchunshu Zhou Guangtao Zeng Antoine Bosselut Mrinmaya Sachan
Recent work has shown that language models (LMs) have strong multi-step
(i.e., procedural) reasoning capabilities. However, it is unclear whether LMs
perform these tasks by cheating with answers memorized from pretraining corpus,
or, via a multi-step reasoning mechanism. In this paper, we try to answer this
question by exploring a mechanistic interpretation of LMs for multi-step
reasoning tasks. Concretely, we hypothesize that the LM implicitly embeds a
reasoning tree resembling the correct reasoning process within it. We test this
hypothesis by introducing a new probing approach (called MechanisticProbe) that
recovers the reasoning tree from the model's attention patterns. We use our
probe to analyze two LMs: GPT-2 on a synthetic task (k-th smallest element),
and LLaMA on two simple language-based reasoning tasks (ProofWriter & AI2
Reasoning Challenge). We show that MechanisticProbe is able to detect the
information of the reasoning tree from the model's attentions for most
examples, suggesting that the LM indeed is going through a process of
multi-step reasoning within its architecture in many cases.