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Computer Science > Computation and Language

arXiv:2306.12672 (cs)
[Submitted on 22 Jun 2023]

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

Authors:Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman
, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum
Download a PDF of the paper titled From Word Models to World Models:
Translating from Natural Language to the Probabilistic Language of
Thought, by Lionel Wong and 6 other authors
Download PDF

    Abstract: 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.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI); Symbolic Computation (cs.SC)
Cite as:  arXiv:2306.12672 [cs.CL]
          (or arXiv:2306.12672v1 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2306.12672
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Lionel Wong [view email]
[v1] Thu, 22 Jun 2023 05:14:00 UTC (6,050 KB)
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