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 rational meaning construction, a computational framework for language-informed thinking that combines neural language models with probabilistic models for rational inference. We frame linguistic meaning as a context-sensitive mapping from natural language into a probabilistic language of thought (PLoT)--a general-purpose symbolic substrate for generative world modeling. Our architecture integrates two computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for commonsense reasoning; and we model meaning construction with 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 through examples covering four core domains from cognitive science: probabilistic reasoning, logical and relational reasoning, visual and physical reasoning, and social reasoning. 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 (physics simulators, graphics engines, and planning algorithms) to provide a unified commonsense thinking interface from language. Finally, we explore how language can drive the construction of world models themselves. We hope this work will provide a roadmap towards cognitive models and AI systems that synthesize the insights of both modern and classical computational perspectives.

翻译：语言如何影响我们的深层思考？具体而言，人类如何从语言中构建意义——我们又该如何利用语言学意义理论，以更接近人类的方式构建具有思考能力的机器？本文提出了一种理性意义构建的计算框架，该框架融合了神经语言模型与用于理性推断的概率模型，旨在实现语言驱动的思维过程。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——PLoT是一种用于生成式世界建模的通用符号基质。本文架构整合了两种此前未曾结合的计算工具：我们利用概率程序（一种用于常识推理的富有表达力的表征方式）对思考进行建模，并借助大型语言模型（LLM）实现意义构建——LLM能够支持从自然语言语句到概率编程语言中代码表达式的广泛覆盖翻译。我们通过涵盖认知科学四大核心领域的示例来阐释本框架：概率推理、逻辑与关系推理、视觉与物理推理，以及社会推理。在每个领域，我们证明LLM能够生成捕捉实用恰当语言意义的语境敏感翻译，同时，基于生成程序的贝叶斯推理可支持连贯且稳健的常识推理。我们将框架扩展至整合具有认知动机的符号模块（物理模拟器、图形引擎和规划算法），从而提供从语言出发的统一常识思考接口。最后，我们探讨语言如何驱动世界模型本身的构建。希望这项工作能为融合现代与经典计算视角的认知模型及人工智能系统提供路线图。