Reasoning is a distinctive human capacity, enabling us to address complex
problems by breaking them down into a series of manageable cognitive steps.
Yet, complex logical reasoning is still cumbersome for language models. Based
on the dual process theory in cognitive science, we are the first to unravel
the cognitive reasoning abilities of language models. Our framework employs an
iterative methodology to construct a Cognitive Tree (CogTree). The root node of
this tree represents the initial query, while the leaf nodes consist of
straightforward questions that can be answered directly. This construction
involves two main components: the implicit extraction module (referred to as
the intuitive system) and the explicit reasoning module (referred to as the
reflective system). The intuitive system rapidly generates multiple responses
by utilizing in-context examples, while the reflective system scores these
responses using comparative learning. The scores guide the intuitive system in
its subsequent generation step. Our experimental results on two popular and
challenging reasoning tasks indicate that it is possible to achieve a
performance level comparable to that of GPT-3.5 (with 175B parameters), using a
significantly smaller language model that contains fewer parameters (<=7B) than
5% of GPT-3.5.

通过使用认知树 (CogTree) 框架，我们揭示了语言模型的认知推理能力，并证明可以使用明显较小的语言模型 (<=7B) 达到与 GPT-3.5 相当的性能水平，而 GPT-3.5 具有更多的参数（175 亿）。