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UnitedQA: A Hybrid Approach for Open Domain Question Answering.

, , , , , and . ACL/IJCNLP (1), page 3080-3090. Association for Computational Linguistics, (2021)

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Transfer Understanding from Head Queries to Tail Queries., , , and . CIKM, page 1299-1308. ACM, (2014)Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs., , , and . CoRR, (2022)Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency., , , , and . CoRR, (2023)OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering., , , , and . NAACL-HLT, page 932-942. Association for Computational Linguistics, (2022)MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation., , , , , and . NAACL-HLT, page 1610-1623. Association for Computational Linguistics, (2022)XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge., , , and . AAAI, page 10840-10848. AAAI Press, (2022)Truncated Diffusion Probabilistic Models., , , and . CoRR, (2022)Competition-Level Problems are Effective LLM Evaluators., , , , , , , , , and 1 other author(s). CoRR, (2023)GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation., , , , , , and . CoRR, (2022)Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models., , , , , , , and . CoRR, (2023)