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Recent work found high mutual information between the learned representationsof large language models (LLMs) and the geospatial property of its input,hinting an emergent internal model of space. However, whether this internalspace model has any causal effects on the LLMs' behaviors was not answered bythat work, led to criticism of these findings as mere statistical correlation.Our study focused on uncovering the causality of the spatial representations inLLMs. In particular, we discovered the potential spatial representations inDeBERTa, GPT-Neo using representational similarity analysis and linear andnon-linear probing. Our casual intervention experiments showed that the spatialrepresentations influenced the model's performance on next word prediction anda downstream task that relies on geospatial information. Our experimentssuggested that the LLMs learn and use an internal model of space in solvinggeospatial related tasks.

More than Correlation: Do Large Language Models Learn Causal Representations of Space?