Same Formulas, Different Semantics: Do Language Models Follow Modal Logic Specifications?

Imagine you're developing a language model that needs to understand complex scenarios involving necessity and possibility, like distinguishing between what could happen versus what must happen. Current evaluations often rely on familiar logical frameworks, which can mislead the model's understanding when faced with nuanced modal problems. This is what's called a failure mode, where models might perform well in one context but struggle in another due to their rigid adherence to familiar logic rather than the actual semantics of the task at hand.

To address this, the authors propose a method that constructs paired modal problems with identical premises but different conditions, allowing for a more nuanced evaluation of how models reason about these scenarios. They found that when models are prompted directly, many perform below a baseline that only considers the semantic conditions. However, by enabling a reasoning mode, one model, DeepSeek V4 Flash, dramatically improved its performance from 4.4% to 88.1%. This indicates that the way models are prompted and the reasoning mode they operate in can significantly influence their ability to follow stipulated modal semantics, which is crucial for building more reliable AI systems.

The paper introduces a new approach to evaluating language models based on modal reasoning.

The evaluation is grounded in automated reasoning and includes a balanced core for testing.