End-to-end Information Extraction from Archival Records with Multimodal Large Language Models

This talk was presented on November 13, 2025 at the 34^th ACM International Conference on Information and Knowledge Management (CIKM 2025) in Seoul, Korea.  

In the paper associated with this presentation, we first introduce BZKOpen, a new annotated dataset designed for key information extraction from historical German index cards. Furthermore, we systematically assess the capabilities of several state-of-the-art MLLMs on the task of extracting key information from these archival documents. We further provide practical insights into prompt engineering and inference settings, offering guidance for applying MLLMs to real-world key information extraction tasks. Additionally, we highlight the need for more ground truth datasets that include a wider range of historical documents with varying quality and in multiple languages, in order to fully explore the potentials and limitations of MLLMs for key information extraction from historical records.