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From Speculation Detection to Trustworthy Relational Tuples in Information Extraction., , , and . EMNLP (Findings), page 13287-13299. Association for Computational Linguistics, (2023)Aligning Crowd Feedback via Distributional Preference Reward Modeling., , , , , and . CoRR, (2024)Syntactic Multi-view Learning for Open Information Extraction., , , and . EMNLP, page 4072-4083. Association for Computational Linguistics, (2022)Tired of Plugins? Large Language Models Can Be End-To-End Recommenders., , , , , , , , , and . CoRR, (2024)Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection., , , and . CoRR, (2023)DocOIE: A Document-level Context-Aware Dataset for OpenIE., , , , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 2377-2389. Association for Computational Linguistics, (2021)A Unified Framework for Multi-Domain CTR Prediction via Large Language Models., , , , , , , , and . CoRR, (2023)DOCoR: Document-level OpenIE with Coreference Resolution., , and . WSDM, page 1204-1207. ACM, (2023)Open Information Extraction via Chunks., , , and . EMNLP, page 15390-15404. Association for Computational Linguistics, (2023)