Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives
This paper introduces SAGE, a framework that combines cognitive models with language models for generating and evaluating alternatives in pragmatic reasoning. Tested on three case studies, SAGE models outperformed baselines but revealed an asymmetry between LM proposers and evaluators.
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[Submitted on 20 Jul 2026]
Title:Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives
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Abstract:Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain. Formal and computational models of pragmatics must therefore specify the sets of alternatives that interlocutors reason over, which is often done through manual specification. Here we propose a framework, ScAffolded Generative models for Explanation (SAGE), that combines the explanatory transparency of cognitive models with the generative flexibility of language models (LMs). SAGE decomposes a pragmatic process into three kinds of modules: proposers, which use LMs to generate an open-ended space of candidate alternatives; evaluators, which assess those alternatives (e.g., their semantics, complexity, or typicality); and selectors, which implement the rule-based computational steps of a cognitively motivated task analysis. We assess SAGE in three case studies spanning pragmatic generation and interpretation-referential expression generation, manner (M-)implicatures, and Gricean conversational implicatures. SAGE models are evaluated critically using established methods from computational cognitive modeling, including ablations, baseline comparisons, and quantitative fit to human data. Across studies, SAGE models achieved high accuracy and often outperformed baselines, but component-level analyses reveal an asymmetry: LM proposers reliably generated alternatives well-suited to pragmatic modeling, whereas LM evaluators are better at providing intuitive judgements rather than judgements of theoretical or formal measures. We discuss the promise and the limitations of neuro-symbolic models as candidate explanatory accounts of human pragmatic language use.
Comments: 27 pages main text, 9 figures; 25 pages supplementary materials
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.18443 [cs.CL]
(or arXiv:2607.18443v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.18443
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Polina Tsvilodub [view email] [v1] Mon, 20 Jul 2026 18:51:46 UTC (11,548 KB)
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