是什么让语言易于深度学习？深度神经网络与人类同样受益于组合结构
What makes a language easy to deep-learn? Deep neural networks and humans similarly benefit from compositional structure
AI总结：
本文通过让大型语言模型和循环神经网络学习组合程度不同的语言，证明二者均像人类一样更易学习组合性强的语言，并表现出更系统的泛化与更高的一致性。
摘要：
深度神经网络推动了自然语言处理的成功。语言的一个基本属性是其组合结构，它使人类能够系统性地为新意义生成形式。对人类而言，具有更具组合性和透明结构的语言通常比那些不透明且不规则的语言更容易学习。然而，这种可学习性优势尚未在深度神经网络中得到证明，从而限制了它们作为人类语言学习模型的用途。在此，我们直接测试神经网络在学习和泛化组合结构程度不同的各种语言时如何与人类进行比较。我们评估了一个大型语言模型和循环神经网络的记忆与泛化能力，并表明两种深度神经网络都对更具结构化的语言输入表现出可学习性优势：接触更具组合性语言的神经网络展现出更系统的泛化、不同智能体之间更高的一致性，以及与人类学习者更高的相似性。
英文摘要：
Deep neural networks drive the success of natural language processing. A fundamental property of language is its compositional structure, allowing humans to systematically produce forms for new meanings. For humans, languages with more compositional and transparent structures are typically easier to learn than those with opaque and irregular structures. However, this learnability advantage has not yet been shown for deep neural networks, limiting their use as models for human language learning. Here, we directly test how neural networks compare to humans in learning and generalizing different languages that vary in their degree of compositional structure. We evaluate the memorization and generalization capabilities of a large language model and recurrent neural networks, and show that both deep neural networks exhibit a learnability advantage for more structured linguistic input: neural networks exposed to more compositional languages show more systematic generalization, greater agreement between different agents, and greater similarity to human learners.
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。
