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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)GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions., , , , , , , and . CoRR, (2023)GENIUS: Sketch-based Language Model Pre-training via Extreme and Selective Masking for Text Generation and Augmentation., , , , , , and . CoRR, (2022)BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining., , , , , , , , , and 2 other author(s). CoRR, (2020)MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation., , , , , and . NAACL-HLT, page 1610-1623. Association for Computational Linguistics, (2022)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)Truncated Diffusion Probabilistic Models., , , and . CoRR, (2022)Competition-Level Problems are Effective LLM Evaluators., , , , , , , , , and 1 other author(s). CoRR, (2023)