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January 3, 2025
Foundation models have made remarkable progress in recent years in areas such as autonomous tool use and reasoning. However, their abilities to handle location-based information and maps, which are essential for everyday applications like navigation, resource finding, and logistics optimization, have not been systematically investigated. To address this gap, MapEval was developed, a benchmark for evaluating the ability of foundation models to answer diverse and complex map-based user queries with geographic reasoning.
MapEval encompasses three task types: text-based, API-based, and visual. These require gathering information through map tools, processing heterogeneous geographic contexts (e.g., named entities, travel distances, user ratings, images), and compositional reasoning – all challenges for current foundation models. With 700 questions across locations in 180 cities and 54 countries, MapEval assesses the models' ability to handle spatial relationships, map graphics, trip planning, and navigation problems.
As part of MapEval, 28 leading foundation models were comprehensively tested. Although no single model excelled in all tasks, Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro achieved competitive results overall. However, significant performance differences were observed, particularly in MapEval, where agents using Claude-3.5-Sonnet, GPT-4o, and Gemini-1.5-Pro outperformed by 16% and 21% respectively. The differences were even larger compared to open-source LLMs.
Detailed analyses provide insights into the strengths and weaknesses of current models. However, all models remained on average more than 20% behind human performance and struggled with complex map images and demanding geographic reasoning. This gap underscores the importance of MapEval for the further development of foundation models with a better understanding of geographic information.
The development and application of benchmarks like MapEval plays a crucial role in the advancement of artificial intelligence. Mindverse, as a German provider of AI-powered content tools, recognizes the importance of such benchmarks for the development and optimization of AI solutions. MapEval offers valuable insights for improving AI systems that can be integrated into the tools offered by Mindverse, such as chatbots, voicebots, AI search engines, and knowledge systems. By combining powerful foundation models with a sound geographic understanding, innovative applications in areas such as navigation, logistics, and resource management can emerge.
The ability to process and interpret geographic information is crucial for many AI applications. From optimizing delivery routes to personalized location recommendations, a robust geographic understanding enables AI systems to provide contextual and relevant information. MapEval contributes to pushing the boundaries of what is possible and driving the development of AI solutions that enrich our daily lives and solve complex challenges.
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