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Code Execution with Pre-trained Language Models.

, , , , , , , and . ACL (Findings), page 4984-4999. Association for Computational Linguistics, (2023)

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Transfer Understanding from Head Queries to Tail Queries., , , and . CIKM, page 1299-1308. ACM, (2014)Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency., , , , and . CoRR, (2023)Mixing and Shifting: Exploiting Global and Local Dependencies in Vision MLPs., , , and . CoRR, (2022)Competition-Level Problems are Effective LLM Evaluators., , , , , , , , , and 1 other author(s). CoRR, (2023)Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models., , , , , , , and . CoRR, (2023)Truncated Diffusion Probabilistic Models., , , and . CoRR, (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)MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation., , , , , and . NAACL-HLT, page 1610-1623. Association for Computational Linguistics, (2022)BANG: Bridging Autoregressive and Non-autoregressive Generation with Large Scale Pretraining., , , , , , , , , and 2 other author(s). CoRR, (2020)GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions., , , , , , , and . CoRR, (2023)