Go to ACL ARR 2025 February homepageSpatial Representation of Large Language Models in 2D Scene
Abstract: Spatial representations are fundamental to human cognition, as understanding spatial relationships between objects is essential in daily life. Language serves as an indispensable tool for communicating spatial information, creating a close connection between spatial representations and spatial language. Large language models (LLMs), theoretically, possess spatial cognition due to their proficiency in natural language processing. This study examines the spatial representations of LLMs by employing traditional spatial tasks used in human experiments and comparing the models' performance to that of humans. The results indicate that LLMs resemble humans in selecting spatial prepositions to describe spatial relationships and exhibit a preference for vertically oriented spatial terms. However, the human tendency to better represent locations along specific axes is absent in the performance of LLMs. This finding suggests that, although spatial language is closely linked to spatial representations, the two are not entirely equivalent.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: large language model, spatial representation, relationship, evaluation, 2D scene
Contribution Types: Reproduction study
Languages Studied: English
Submission Number: 226
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