构建1万+文化文物数据集，评估AI理解历史遗产的能力
Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts
- 构建涵盖266种文化的10,250个专家验证样本
- 首次系统评测大模型对古籍、艺术品等的历史理解水平
- 适合历史学者、考古学家及文化遗产保护者使用
理解历史与文化遗产需要专业知识和先进计算技术，但过程仍复杂耗时。尽管大型多模态模型提供了潜在支持，其评估与改进仍需标准化基准。为此，我们提出TimeTravel，一个包含10,250个专家验证样本的基准数据集，覆盖10个主要历史区域中的266种不同文化。该数据集专为人工智能分析手稿、艺术品、铭文和考古发现设计，提供结构化数据与稳健评估框架，用于评估模型在分类、解释和历史理解方面的能力。通过融合人工智能与历史研究，TimeTravel推动面向历史学家、考古学家、研究人员和文化旅游者的智能化工具发展，助力提取有价值信息，确保技术真正服务于历史发现与文化遗产保护。我们在TimeTravel上评估了当前主流AI模型，揭示其优势与不足。目标是使AI成为文化遗产保护的可靠伙伴。
原文摘要 · Abstract (English)
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}.
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