  Embodied language comprehension emphasizes that language understanding is not
solely a matter of mental processing in the brain but also involves
interactions with the physical and social environment. With the explosive
growth of Large Language Models (LLMs) and their already ubiquitous presence in
our daily lives, it is becoming increasingly necessary to verify their
real-world understanding. Inspired by cognitive theories, we propose POSQA: a
Physical Object Size Question Answering dataset with simple size comparison
questions to examine the extremity and analyze the potential mechanisms of the
embodied comprehension of the latest LLMs.
  We show that even the largest LLMs today perform poorly under the zero-shot
setting. We then push their limits with advanced prompting techniques and
external knowledge augmentation. Furthermore, we investigate whether their
real-world comprehension primarily derives from contextual information or
internal weights and analyse the impact of prompt formats and report bias of
different objects. Our results show that real-world understanding that LLMs
shaped from textual data can be vulnerable to deception and confusion by the
surface form of prompts, which makes it less aligned with human behaviours.
