What question did this study set out to answer?

The central aim is to address the logical reasoning deficiencies of large language models, particularly with conditionals and modals.

March 23, 2026Open Access

LogiCue: Targeted Prompting for Improved Modal and Conditional Reasoning in Large Language Models

Key Points

The central aim is to address the logical reasoning deficiencies of large language models, particularly with conditionals and modals.
Investigated seven inference patterns specifically involving modals and conditionals
Conducted targeted logical analysis to identify reasoning failures
Developed the LogiCue framework as a prompting approach to improve reasoning in language models
Identified distinct error types in reasoning involving conditionals and modals
Proposed the LogiCue framework as a potential solution to these reasoning challenges

Abstract

Large language models increasingly serve as primary information sources across critical domains, yet fundamental limitations persist in their logical reasoning capabilities. Even state-of-the-art models systematically fail on inference patterns involving conditionals and modals (might, must), the fundamental linguistic tools humans use to reason about possibilities, necessities, and hypothetical scenarios. Recent research reveals that language models make distinct types of errors with conditionals versus modals, both of which require immediate attention. This study investigates seven challenging inference patterns through targeted logical analysis and proposes the LogiCue framework, a pattern-specific prompting approach that addresses the root causes of these reasoning failures.

LogiCue: Targeted Prompting for Improved Modal and Conditional Reasoning in Large Language Models

Key Points

Abstract

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