Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866929722789724160 |
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| author | Ghaboura, Sara More, Ketan Thawkar, Ritesh Alghallabi, Wafa Thawakar, Omkar Khan, Fahad Shahbaz Cholakkal, Hisham Khan, Salman Anwer, Rao Muhammad |
| author_facet | Ghaboura, Sara More, Ketan Thawkar, Ritesh Alghallabi, Wafa Thawakar, Omkar Khan, Fahad Shahbaz Cholakkal, Hisham Khan, Salman Anwer, Rao Muhammad |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14865 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts Ghaboura, Sara More, Ketan Thawkar, Ritesh Alghallabi, Wafa Thawakar, Omkar Khan, Fahad Shahbaz Cholakkal, Hisham Khan, Salman Anwer, Rao Muhammad Computer Vision and Pattern Recognition Machine Learning 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}. |
| title | Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.14865 |