How does language inform our downstream thinking? In particular, how do humans make meaning from language -- and how can we leverage a theory of linguistic meaning to build machines that think in more human-like ways? In this paper, we propose \textit{rational meaning construction}, a computational framework for language-informed thinking that combines neural models of language with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a \textit{probabilistic language of thought} (PLoT) -- a general-purpose symbolic substrate for probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with \textit{probabilistic programs}, an expressive representation for flexible commonsense reasoning; and we model meaning construction with \textit{large language models} (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language. We illustrate our framework in action through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning about agents and their plans. In each, we show that LLMs can generate context-sensitive translations that capture pragmatically-appropriate linguistic meanings, while Bayesian inference with the generated programs supports coherent and robust commonsense reasoning. We extend our framework to integrate cognitively-motivated symbolic modules to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves.

翻译：语言如何影响我们的深层思维？特别是，人类如何从语言中构建意义——我们又该如何利用语言意义理论来构建更接近人类思维方式的机器？本文提出**理性意义构建**这一计算框架，将神经语言模型与概率推理的理性模型相结合，实现语言驱动的思维方式。我们将语言意义定义为从自然语言到**概率思维语言**（PLoT）的语境敏感映射——这是一种用于概率生成式世界建模的通用符号基底。我们的架构整合了两种此前未曾结合的强大计算工具：我们利用**概率程序**（一种用于灵活常识推理的表达性表征）来建模思维过程；同时利用**大语言模型**（LLMs）进行意义构建，支持从自然语言语句到概率编程语言代码表达式的广泛覆盖翻译。我们通过认知科学四个核心领域的实例展示该框架的应用：概率推理、逻辑与关系推理、视觉与物理推理，以及关于智能体及其计划的社会推理。在每个领域中，我们证明LLMs能够生成捕捉语用恰当语言意义的语境敏感翻译，而基于生成程序的贝叶斯推理则支持连贯且稳健的常识推理。我们进一步扩展框架，整合认知驱动的符号模块，提供从语言出发的统一常识思维接口。最后，我们探讨语言如何驱动世界模型本身的构建。