Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
This provides a structured evaluation framework for AI models to assist historians, archaeologists, and others in analyzing artifacts, though it is incremental as it focuses on benchmarking rather than new methods.
The authors tackled the lack of a standardized benchmark for evaluating large multimodal models on historical and cultural artifacts by introducing TimeTravel, a dataset of 10,250 expert-verified samples across 266 cultures, and found that contemporary AI models show strengths but need improvement in classification, interpretation, and historical comprehension.
Understanding historical and cultural artifacts demands human expertise and advanced computational techniques, yet the process remains complex and time-intensive. While large multimodal models offer promising support, their evaluation and improvement require a standardized benchmark. To address this, we introduce TimeTravel, a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions. Designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries, TimeTravel provides a structured dataset and robust evaluation framework to assess AI models' capabilities in classification, interpretation, and historical comprehension. By integrating AI with historical research, TimeTravel fosters AI-powered tools for historians, archaeologists, researchers, and cultural tourists to extract valuable insights while ensuring technology contributes meaningfully to historical discovery and cultural heritage preservation. We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement. Our goal is to establish AI as a reliable partner in preserving cultural heritage, ensuring that technological advancements contribute meaningfully to historical discovery. Our code is available at: \url{https://github.com/mbzuai-oryx/TimeTravel}.