Empower Nested Boolean Logic via Self-Supervised Curriculum Learning (2310.05450v2)

Abstract: Beyond the great cognitive powers showcased by LLMs, it is crucial to scrutinize whether their reasoning capabilities stem from strong generalization or merely exposure to relevant data. As opposed to constructing increasingly complex logic, this paper probes into the boolean logic, the root capability of a logical reasoner. We find that any pre-trained LLMs even including LLMs only behave like a random selector in the face of multi-nested boolean logic, a task that humans can handle with ease. To empower LLMs with this fundamental capability, this paper proposes a new self-supervised learning method \textit{Curriculum Logical Reasoning} (\textsc{Clr}), where we augment the training data with nested boolean logic chain step-by-step, and program the training from simpler logical patterns gradually to harder ones. This new training paradigm allows LLMs to effectively generalize to much harder and longer-hop logic, which can hardly be learned through naive training. Furthermore, we show that boolean logic is a great foundation for improving the subsequent general logical tasks.