Recently, large-scale transformer-based models have been proven to be effective over various tasks across many domains. Nevertheless, applying them in industrial production requires tedious and heavy works to reduce inference costs. To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels. First, we design highly optimized kernels for long inputs and large hidden sizes. Second, we propose a flexible CUDA memory manager to reduce the memory footprint when deploying a large model. Compared with the state-of-the-art transformer inference library (Faster Transformer v4.0), EET can achieve an average of 1.40-4.20x speedup on the transformer decoder layer with an A100 GPU
Towards Scalable and Reliable Capsule Networks for Challenging NLP Applications
Technische Universität Darmstadt（达姆施塔特工业大学）；Nanyang Technological University（南洋理工大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
Enhancing NLP Robustness and Generalization through LLM-Generated Contrast Sets: A Scalable Framework for Systematic Evaluation and Adversarial Training
College of Natural Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校自然科学学院）
Masader Plus: A New Interface for Exploring +500 Arabic NLP Datasets
King’s College London（伦敦国王学院）；Amazon（亚马逊）；University of Bahrain（巴林大学）；IIT Madras（印度理工学院马德拉斯分校）；Dasman Diabetes Institute（达斯曼糖尿病研究所）；King Abdulaziz University（阿卜杜勒阿齐兹国王大学）；KFUPM（法赫德国王石油与矿业大学）；University of Tübingen（图宾根大学）；INSEA（摩洛哥国家统计与应用经济学院）；University of Cape Town（开普敦大学）；Hugging Face, Inc（Hugging Face公司）；Saudi Electronic University（沙特电子大学）
PSDVec: a Toolbox for Incremental and Scalable Word Embedding
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY), Nanyang Technological University（南洋理工大学）；Tsinghua University（清华大学）
Easy-to-Hard Generalization: Scalable Alignment Beyond Human Supervision
Carnegie Mellon University（卡内基梅隆大学）；Peking University（北京大学）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；University of Cambridge（剑桥大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；UMass Amherst（马萨诸塞大学阿默斯特分校）
Learn to Memorize: Scalable Continual Learning in Semiparametric Models with Mixture-of-Neighbors Induction Memory
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；Microsoft（微软公司）；Microsoft Research Asia（微软亚洲研究院）
