從詞模型到世界模型：從自然語言翻譯到思維的概率語言

摘要

語言如何影響我們的下游思維？特別是，人類如何從語言中獲得意義，以及我們如何利用語言意義理論來建構更符合人類思維方式的機器？在本文中，我們提出了理性意義建構，這是一個結合了語言神經模型和理性推論概率模型的計算框架，用於基於語言的思維。我們將語言意義框架化為一種從自然語言到概率思維語言（PLoT）的上下文敏感映射，這是一種用於概率生成世界建模的通用符號底層。我們的架構整合了兩個強大的計算工具，這兩者以前從未結合過：我們使用概率程序來建模思維，這是一種靈活的常識推理表達方式；我們使用大型語言模型（LLMs）來建模意義構建，這些模型支持從自然語言表達轉換為概率編程語言中的代碼表達。我們通過涵蓋認知科學的四個核心領域的示例來展示我們的框架：概率推理、邏輯和關係推理、視覺和物理推理，以及關於代理人及其計劃的社會推理。在每個示例中，我們展示了LLMs可以生成捕捉實際適當語言意義的上下文敏感翻譯，同時通過生成的程序進行的貝葉斯推理支持一致且強大的常識推理。我們擴展了我們的框架，以整合基於認知動機的符號模塊，提供一個從語言到統一常識思維界面的接口。最後，我們探討了語言如何推動世界模型本身的構建。

English

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 models of language 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 probabilistic, generative world modeling. Our architecture integrates two powerful computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for flexible 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 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.

從詞模型到世界模型：從自然語言翻譯到思維的概率語言

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought

摘要

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