Meaningful Learning: Advancing Abstract Reasoning in Large Language Models via Generic Fact Guidance

arXiv:2403.09085v1 Announce Type: new
Abstract: Large language models (LLMs) have developed impressive performance and strong explainability across various reasoning scenarios, marking a significant stride towards mimicking human-like intelligence. Despite this, when tasked with simple questions supported by a generic fact, LLMs often fail to provide consistent and precise answers, indicating a deficiency in abstract reasoning abilities. This has sparked a vigorous debate about whether LLMs are genuinely reasoning or merely memorizing. In light of this, we design a preliminary study …

abstract arxiv consistent cs.ai cs.cl explainability guidance human human-like intelligence language language models large language large language models llms performance questions reasoning simple type via