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

2026-03-09

AI Summary

This study addresses the “proviso problem” in pragmatics—the discrepancy between theoretical predictions and human intuitions regarding presuppositions in conditional sentences—by constructing the first natural language inference diagnostic dataset specifically targeting this issue and introducing a multi-faceted evaluation framework. Through experiments and interpretability analyses on mainstream language models including RoBERTa, DeBERTa, LLaMA, and Gemma, the work reveals that while model performance broadly aligns with human judgments, it largely relies on shallow pattern matching rather than genuine pragmatic reasoning. The research establishes a novel paradigm for evaluating context-dependent semantic and pragmatic comprehension in language models and exposes fundamental limitations in their ability to handle such complex linguistic phenomena.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• proviso problem
• presupposition projection
• conditional sentences
• pragmatic competence
• language models

Innovation

• proviso problem
• presupposition projection
• natural language inference
• pragmatic competence
• diagnostic dataset