Implicit Chain of Thought Reasoning via Knowledge Distillation
Yuntian Deng Kiran Prasad Roland Fernandez Paul Smolensky Vishrav Chaudhary Stuart Shieber
To augment language models with the ability to reason, researchers usually
prompt or finetune them to produce chain of thought reasoning steps before
producing the final answer. However, although people use natural language to
reason effectively, it may be that LMs could reason more effectively with some
intermediate computation that is not in natural language. In this work, we
explore an alternative reasoning approach: instead of explicitly producing the
chain of thought reasoning steps, we use the language model's internal hidden
states to perform implicit reasoning. The implicit reasoning steps are
distilled from a teacher model trained on explicit chain-of-thought reasoning,
and instead of doing reasoning "horizontally" by producing intermediate words
one-by-one, we distill it such that the reasoning happens "vertically" among
the hidden states in different layers. We conduct experiments on a multi-digit
multiplication task and a grade school math problem dataset and find that this
approach enables solving tasks previously not solvable without explicit
chain-of-thought, at a speed comparable to no chain-of-thought.