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This work systematically evaluates whether explanations are causally tied to model predictions within a single generation step under a controlled experimental setup, explicitly eliminating unnecessary chain-of-thought or other intermediate reasoning processes across knowledge-intensive QA, visual entailment, and compos...
Event extraction is fundamental to information extraction. Prior approaches often separate event detection and argument extraction or depend on dataset-specific designs, limiting scalability and cross-domain generalization. We propose a unified generative sequence-to-sequence framework that performs event extraction su...
This work proposes a unified multi-domain and multi-task training framework that models heterogeneous event schemas within a single model, enabling dynamic adaptation to dataset-specific schemas without requiring complete event label sets at inference time.
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