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ROBE: Reversed-Order-Biased-Experts for Extracting Extreme Long-tail Events from Historical Texts

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries. The methods we propose aim to tackle the impossible: extracting the long-tail of the long-tail. Historic data from before the 19th century is in itself a niche domain not covered in the pre-training of Large Language Models, and we aim to extract events only very scarcely annotated in the training data available for this domain. We propose creating expert classifiers for subgroups of the events present in the training data. We make these groupings based on similar

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