title: From Word Models to World Models: Translating from Natural Language to
    the Probabilistic Language of Thought
summary: 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.
paper_authors._id: 6494ee749a0573a99591e839
paper_authors.name: Lionel Wong
paper_authors._id: 6494ee749a0573a99591e83a
paper_authors.name: Gabriel Grand
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-23T08:45:54.188Z
paper_authors._id: 60b90fdd48cc96f40ef4fda6
paper_authors.avatarUrl: /avatars/a5bbf261fd2f4db784b83b52be014bc5.svg
paper_authors.fullname: Gabe Grand
paper_authors.type: user
paper_authors.user: gabegrand
paper_authors._id: 6494ee749a0573a99591e83b
paper_authors.name: Alexander K. Lew
paper_authors._id: 6494ee749a0573a99591e83c
paper_authors.name: Noah D. Goodman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-15T09:56:00.189Z
paper_authors._id: 67321274c1f20c742bcf7a8d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ltcQhre6eDRVzn6Vbbyhu.png
paper_authors.fullname: Noah D. Goodman
paper_authors.type: user
paper_authors.user: ngoodman
paper_authors._id: 6494ee749a0573a99591e83d
paper_authors.name: Vikash K. Mansinghka
paper_authors._id: 6494ee749a0573a99591e83e
paper_authors.name: Jacob Andreas
paper_authors._id: 6494ee749a0573a99591e83f
paper_authors.name: Joshua B. Tenenbaum
paper_title: From Word Models to World Models: Translating from Natural Language to
    the Probabilistic Language of Thought
paper_summary: 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.
paper_ai_keywords: probabilistic language of thought (PLoT) probabilistic programs large language models (LLMs) Broad-coverage-translation natural language utterances code expressions Bayesian inference probabilistic reasoning logical and relational reasoning visual and physical reasoning social reasoning pragmatically-appropriate linguistic meanings commonsense thinking interface cognitively-motivated symbolic modules world models