有意义的学习：通过通用事实引导增强大型语言模型的抽象推理能力
Meaningful Learning: Enhancing Abstract Reasoning in Large Language Models via Generic Fact Guidance
AI总结：
针对大型语言模型在抽象推理上的不足，本文定制了AbsR数据集并提出有意义的学习范式，通过通用事实引导有效提升了模型的一般与抽象推理能力。
摘要：
大型语言模型（LLMs）在各种推理场景中展现出了令人印象深刻的性能和强大的可解释性，标志着在模仿人类智能方面迈出了重要一步。尽管如此，当面对由通用事实支撑的几个简单问题时，LLMs往往难以抽象并应用这些通用事实来提供一致且精确的答案，这暴露了其在抽象推理能力上的不足。这引发了关于LLMs是真正在进行推理还是仅仅在记忆的激烈争论。鉴于此，我们设计了一项初步研究，以量化并深入探讨现有LLMs的抽象推理能力。我们的发现揭示了它们在一般推理和抽象推理表现之间存在显著差距。为缓解这一问题，我们定制了一个抽象推理数据集（AbsR），并提出了一种有意义的学习范式，以教会LLMs如何利用通用事实进行推理。结果表明，我们的方法不仅提升了LLMs的一般推理性能，还在其抽象推理能力上取得了相当大的进步，使其超越了简单的记忆或模仿，转向对通用事实的更细致理解和应用。代码可在 https://github.com/Waste-Wood/MeanLearn 获取。
英文摘要：
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 several simple questions supported by a generic fact, LLMs often struggle to abstract and apply the generic fact to provide consistent and precise answers, revealing 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 to quantify and delve into the abstract reasoning abilities of existing LLMs. Our findings reveal a substantial discrepancy between their general reasoning and abstract reasoning performances. To relieve this problem, we tailor an abstract reasoning dataset (AbsR) together with a meaningful learning paradigm to teach LLMs how to leverage generic facts for reasoning purposes. The results show that our approach not only boosts the general reasoning performance of LLMs but also makes considerable strides towards their capacity for abstract reasoning, moving beyond simple memorization or imitation to a more nuanced understanding and application of generic facts. The code is available at https://github.com/Waste-Wood/MeanLearn.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
