Spatial intelligence and contextual relevance in AI-driven health information retrieval

Abstract

The evolution of large language models (LLMs) has already significantly influenced online health information retrieval. As these models gain more widespread use, it is important to understand their ability to contextualize responses based on spatial and geographic information. This study investigates whether LLMs can vary responses based on geographic and spatial context. Using a structured set of prompts submitted to ChatGPT, responses were analyzed to discern patterns based on prompt question and geographic identifiers included in queries. The analysis used word frequency analysis and bidirectional encoder representations from transformers (BERT) embeddings to evaluate the variation in responses concerning geographic specificity. The results provide some evidence that LLMs can generate geographically tailored responses when the query specifies such a need, thereby supporting localized information retrieval. Moreover, prompt responses exhibit an association between spatial distance and word frequency/sentence embedding differences between texts. This result suggests a nuanced representation of spatial information, which could impact user experience by providing more relevant health information based on the user's location. This study lays the groundwork for further exploration into the spatial intelligence of LLMs and their impact on the accessibility of health information online.