斯坦福大学研究大语言模型中的推理何时和为何有帮助的问题

标题：Why think step-by-step? Reasoning emerges from the locality of experience

[Stanford University]

简介：

人类有一种强大而神秘的推理能力。通过一系列纯粹的思维步骤，我们可以做出我们无法直接做出的推论–尽管事实上我们没有从世界上获得额外的数据。同样地，大型语言模型可以通过思维链推理在复杂的任务中表现得更好，在回答问题之前，它们会产生中间步骤。

我们使用语言模型来研究推理何时和为何有帮助的问题，测试了这样的假设：当训练数据由相互影响强烈的局部变量群组成时，推理是有效的。这些训练条件能够使准确的局部推理连锁化，以估计训练中没有一起出现的变量之间的关系。我们对来自贝叶斯网定义的联合分布的样本进行自回归变换器的训练，但只包括每个样本中所有变量的一个子集。

我们比较了有无中间推理步骤的语言模型匹配条件概率的能力，发现只有当训练数据在变量之间的依赖关系方面是局部结构时，中间步骤才有帮助。此外，中间变量需要与观察信息和目标推理之间的关系相关。我们的结果说明了训练数据的统计结构如何驱动推理步骤的有效性。

https://arxiv.org/pdf/2304.03843.pdf

斯坦福大学研究大语言模型中的推理何时和为何有帮助的问题

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