Exploring Geographic Relative Space in Large Language Models through Activation Patching

2026-05-14

AI Summary

Although large language models (LLMs) are increasingly applied to geographic tasks, their internal mechanisms for representing and reasoning about relative spatial relationships remain poorly understood, raising concerns about reliability and safety. This study pioneers the application of mechanistic interpretability techniques—such as activation patching—to the analysis of geospatial cognition in LLMs, systematically investigating how relative positional information is encoded within model internals. The findings uncover key neural pathways and representational strategies employed by LLMs to process geographic spatial relations, offering critical theoretical insights and technical foundations for the trustworthy deployment of geographic artificial intelligence systems.

Abstract

The increased use of Large Language Models (LLMs) in geography raises substantial questions about the safety of integrating these tools across a wide range of processes and analyses, given our very limited understanding of their inner workings. In this extended abstract, we examine how LLMs process relative geographic space using activation patching, an emerging tool for mechanistic interpretability.

Problem

• Large Language Models
• geographic space
• activation patching
• mechanistic interpretability
• geography

Innovation

• activation patching
• Large Language Models
• geographic space
• mechanistic interpretability
• relative spatial reasoning