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_summary: A framework combining large language models and probabilistic programs translates language into a symbolic language of thought for rational, commonsense reasoning across multiple cognitive domains.
paper_ai_keywords: rational meaning construction probabilistic language of thought (PLoT) probabilistic programs large language models (LLMs) probabilistic reasoning logical and relational reasoning visual and physical reasoning social reasoning Bayesian inference symbolic modules world models