Presupposition and Reasoning in Conditionals: A Theory-Based Study of Humans and LLMs

TL;DR

This study compares human and large language model judgments on presupposition projection in conditionals, revealing differences in pragmatic reasoning and highlighting the need for linguistically grounded benchmarks.

Contribution

It introduces a novel evaluation framework comparing humans and LLMs on presupposition in conditionals, emphasizing the importance of linguistic theory-based benchmarks.

Findings

Humans integrate probabilistic and pragmatic cues in judgments.

LLMs show variable alignment with human judgment patterns.

Models with better reasoning often lack human-like pragmatic understanding.

Abstract

Presupposition projection in conditionals is central to theories of meaning and pragmatics, yet it remains largely unevaluated in large language models. We address this gap through a parallel behavioral study comparing human judgments and LLM predictions on a normed dataset of conditional sentences that controls the relation between the antecedent and the projected presupposition. We collect likelihood ratings from 120 participants and four LLMs under matched contextual conditions. Results show that humans integrate probabilistic and pragmatic cues in their judgment, whereas LLMs show variable alignment with human patterns. Using a linguistically motivated checklist within an LLM-as-a-Judge framework, we further evaluate model reasoning. We observe models that best match human ratings often lack coherent pragmatic reasoning, while models with stronger reasoning produce less human-like judgments. These findings suggest that LLMs' performance on such tasks may result from surface pattern matching rather than pragmatic competence. Our findings highlight the importance of benchmarks grounded in linguistic theory for comparing humans and models.