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Reasoning is a distinctive human capacity, enabling us to address complexproblems by breaking them down into a series of manageable cognitive steps.Yet, complex logical reasoning is still cumbersome for language models. Basedon the dual process theory in cognitive science, we are the first to unravelthe cognitive reasoning abilities of language models. Our framework employs aniterative methodology to construct a Cognitive Tree (CogTree). The root node ofthis tree represents the initial query, while the leaf nodes consist ofstraightforward questions that can be answered directly. This constructioninvolves two main components: the implicit extraction module (referred to asthe intuitive system) and the explicit reasoning module (referred to as thereflective system). The intuitive system rapidly generates multiple responsesby utilizing in-context examples, while the reflective system scores theseresponses using comparative learning. The scores guide the intuitive system inits subsequent generation step. Our experimental results on two popular andchallenging reasoning tasks indicate that it is possible to achieve aperformance level comparable to that of GPT-3.5 (with 175B parameters), using asignificantly smaller language model that contains fewer parameters (<=7B) than5% of GPT-3.5.

From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models