Do Language Models Know Theo Has a Wife? Investigating the Proviso Problem

Fuente: arXiv
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Azin, Tara, Dumitrescu, Daniel, Inkpen, Diana, Singh, Raj
Format: Preprint
Veröffentlicht: 2026
Schlagworte:
Online-Zugang:
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
_version_ 1866917324965019648
author Azin, Tara
Dumitrescu, Daniel
Inkpen, Diana
Singh, Raj
author_facet Azin, Tara
Dumitrescu, Daniel
Inkpen, Diana
Singh, Raj
contents We investigate how language models handle the proviso problem, an unresolved issue in pragmatics where presuppositions in conditional sentences diverge between theoretical and human interpretations. We reformulate this phenomenon as a Natural Language Inference task and introduce a diagnostic dataset designed to probe presupposition projection in conditionals. We evaluate RoBERTa, DeBERTa, LLaMA, and Gemma using explainability analyses. The results show that models broadly align with human judgments but rely on shallow pattern matching rather than semantic or pragmatic reasoning. Our work provides the first computational evaluation framework for the proviso problem and highlights the need for diagnostic, multi-method approaches to assess pragmatic competence and context-dependent meaning in language models.
format Preprint
id arxiv_https___arxiv_org_abs_2603_08358
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Do Language Models Know Theo Has a Wife? Investigating the Proviso Problem
Azin, Tara
Dumitrescu, Daniel
Inkpen, Diana
Singh, Raj
Computation and Language
We investigate how language models handle the proviso problem, an unresolved issue in pragmatics where presuppositions in conditional sentences diverge between theoretical and human interpretations. We reformulate this phenomenon as a Natural Language Inference task and introduce a diagnostic dataset designed to probe presupposition projection in conditionals. We evaluate RoBERTa, DeBERTa, LLaMA, and Gemma using explainability analyses. The results show that models broadly align with human judgments but rely on shallow pattern matching rather than semantic or pragmatic reasoning. Our work provides the first computational evaluation framework for the proviso problem and highlights the need for diagnostic, multi-method approaches to assess pragmatic competence and context-dependent meaning in language models.
title Do Language Models Know Theo Has a Wife? Investigating the Proviso Problem
topic Computation and Language
url https://arxiv.org/abs/2603.08358