REVIEW 4 cited by
Evaluating the World Model Implicit in a Generative Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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 to failures. 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.
Forward citations
Cited by 4 Pith papers
-
What Does it Mean for a Neural Network to Learn a "World Model"?
Defines a world model as a simple commutative-diagram factorization through an intermediate representation, with conditions that the model be learned and emergent rather than inherited from input or output.
-
Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
A hybrid language-model and probabilistic-program architecture predicts human judgments on novel open-world reasoning vignettes better than language-model-only baselines.
-
Potemkin Understanding in Large Language Models
LLMs frequently pass definition questions yet fail to use the same concepts in classification, generation, and editing tasks, a gap the authors call potemkin understanding.
-
Quo Vadis, World Modeling?
An agent-centric reframing of world modeling, replacing physical state prediction with 'information transitions' organized into six proxy functions and three empowerment levels.
Discussion (0). Sign in to comment.