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Towards A Unified View of Sparse Feed-Forward Network in Pretraining Large Language Model.

, , , , and . EMNLP, page 15038-15061. Association for Computational Linguistics, (2023)

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Bridging the domain gap in cross-lingual document classification., , , and . CoRR, (2019)Toward Opinion Summarization: Linking the Sources, and . Proceedings of the Workshop on Sentiment and Subjectivity in Text, page 9--14. Sydney, Australia, Association for Computational Linguistics, (July 2006)Improving In-Context Few-Shot Learning via Self-Supervised Training., , , , , , and . NAACL-HLT, page 3558-3573. Association for Computational Linguistics, (2022)Topic Identification for Fine-Grained Opinion Analysis., and . COLING, page 817-824. (2008)Self-training Improves Pre-training for Natural Language Understanding., , , , , , , and . NAACL-HLT, page 5408-5418. Association for Computational Linguistics, (2021)Simple Fusion: Return of the Language Model., , and . WMT, page 204-211. Association for Computational Linguistics, (2018)SemEval-2015 Task 10: Sentiment Analysis in Twitter., , , , , and . SemEval@NAACL-HLT, page 451-463. The Association for Computer Linguistics, (2015)Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models., , , , , , , and . EACL, page 2706-2723. Association for Computational Linguistics, (2023)LEVER: Learning to Verify Language-to-Code Generation with Execution., , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 26106-26128. PMLR, (2023)Training Trajectories of Language Models Across Scales., , , , , , , and . ACL (1), page 13711-13738. Association for Computational Linguistics, (2023)