Non-literal Understanding of Number Words by Language Models

Understand

Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent.

• We investigate whether large language models (LLMs) interpret numbers similarly, focusing on hyperbole and pragmatic halo effects.
• Through systematic comparison with human data and computational models of pragmatic reasoning, we find that LLMs diverge from human interpretation in striking ways.
• By decomposing pragmatic reasoning into testable components, grounded in the Rational Speech Act framework, we pinpoint where LLM processing diverges from human cognition -- not in prior knowledge, but in reasoning with it.