ChatGPT's Text-Based Architecture Hinders 'Trivial' Map Generation Task

Jevgenijs Kazanins recently highlighted a significant challenge faced by OpenAI's ChatGPT, noting its struggle to generate a map of Southeast Asia complete with country names and population data. In a tweet, Kazanins expressed surprise, stating, > "ChatGPT is struggling with generating a map of Southeast Asia with the names of countries and population. Should be a trivial task🤷🏻‍♂️" This observation underscores a key limitation in the capabilities of large language models when confronted with seemingly straightforward visual and spatial data representation tasks.

While highly proficient in natural language understanding and generation, ChatGPT operates primarily as a text-based artificial intelligence. It does not possess inherent visual processing or graphic rendering capabilities, meaning it cannot "draw" images directly. Instead, for tasks like map creation, the model must generate programming code (e.g., Python scripts using libraries like Matplotlib) that, when executed by external tools, can produce a visual output. This reliance on a textual interface for visual outcomes introduces complexities not immediately apparent to users expecting direct graphical results.

Research into ChatGPT's mapping abilities, such as a study titled "Mapping with ChatGPT," confirms these limitations. The study noted that the AI functions more as a "brain" that provides code, rather than a "hand" that directly plots images. Initial attempts to generate maps often yield unsatisfactory results, requiring multiple follow-up prompts and manual code adjustments for refinement. This process highlights that while the AI can suggest methods, the precise execution of visual tasks remains heavily dependent on external software, stable internet connections, and often, significant user intervention.

Furthermore, the effectiveness of AI-generated maps is constrained by the model's training data cut-off, which for GPT-4 was around September 2021. This means ChatGPT may not have access to the most current geopolitical boundaries or demographic statistics, potentially leading to the inclusion of outdated information or "hallucinations," where the AI generates plausible but factually incorrect details. The perceived "triviality" of generating a map with accurate, up-to-date spatial and demographic data clashes with the complex interplay of text-to-code translation, external tool reliance, and data currency challenges inherent to current LLM architectures, revealing a gap between user expectations and the AI's current visual capabilities.