Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
Sara Ghaboura, Ketan More, Ritesh Thawkar, Wafa Alghallabi, Omkar, Thawakar, Fahad Shahbaz Khan, Hisham Cholakkal, Salman Khan, Rao Muhammad, Anwer

TL;DR
TimeTravel is a comprehensive benchmark dataset designed to evaluate large multimodal models on their ability to analyze and interpret historical and cultural artifacts across diverse cultures and regions.
Contribution
We introduce TimeTravel, a large, expert-verified benchmark dataset for assessing AI models' performance in understanding historical and cultural artifacts.
Findings
Contemporary AI models show varied strengths on the benchmark.
The benchmark reveals specific areas where models need improvement.
TimeTravel facilitates AI integration into historical and cultural research.
Abstract
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…
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Taxonomy
TopicsSemantic Web and Ontologies · Digital and Traditional Archives Management · Natural Language Processing Techniques
