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Computer Science > Computation and Language

arXiv:2406.03689 (cs)
[Submitted on 6 Jun 2024 (v1), last revised 22 Jun 2024 (this
version, v2)]

Title:Evaluating the World Model Implicit in a Generative Model

Authors:Keyon Vafa, Justin Y. Chen, Jon Kleinberg, Sendhil
Mullainathan, Ashesh Rambachan
View a PDF of the paper titled Evaluating the World Model Implicit in
a Generative Model, by Keyon Vafa and 4 other authors
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    Abstract:Recent work suggests that large language models may
    implicitly learn world models. How should we assess this
    possibility? We formalize this question for the case where the
    underlying reality is governed by a deterministic finite
    automaton. This includes problems as diverse as simple logical
    reasoning, geographic navigation, game-playing, and chemistry. We
    propose new evaluation metrics for world model recovery inspired
    by the classic Myhill-Nerode theorem from language theory. We
    illustrate their utility in three domains: game playing, logic
    puzzles, and navigation. In all domains, the generative models we
    consider do well on existing diagnostics for assessing world
    models, but our evaluation metrics reveal their world models to
    be far less coherent than they appear. Such incoherence creates
    fragility: using a generative model to solve related but subtly
    different tasks can lead it to fail badly. Building generative
    models that meaningfully capture the underlying logic of the
    domains they model would be immensely valuable; our results
    suggest new ways to assess how close a given model is to that
    goal.

Subjects: Computation and Language (cs.CL); Artificial Intelligence
          (cs.AI)
Cite as:  arXiv:2406.03689 [cs.CL]
          (or arXiv:2406.03689v2 [cs.CL] for this version)
          https://doi.org/10.48550/arXiv.2406.03689
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Keyon Vafa [view email]
[v1] Thu, 6 Jun 2024 02:20:31 UTC (10,082 KB)
[v2] Sat, 22 Jun 2024 18:23:08 UTC (10,218 KB)
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