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

arXiv:2608.24268 · cs.CL · Submitted 2026-08-25 · Read on arXiv

cs.CL

Submitted: 2026-08-25

Updated: 2026-10-05

Terminology

• Data Augmentation — 4× in this paper · explained in 7 episodes
• Ontology — 4× in this paper · explained in 2 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Generative Model — 3× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• External Validation — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 3 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Mixture of Experts — 1× in this paper · explained in 2 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Exploring the Feasibility of ChatGPT for Event Extraction
• Large Language Models for Data Annotation and Synthesis: A Survey
• Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
• SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains
• Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
• Synthetic continued pretraining

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