Transformer Megatron Decepticons
Transformer models are being extensively investigated for various sequence processing tasks, moving beyond natural language processing to encompass time series forecasting, image recognition, and scientific computing applications like solving partial differential equations. Current research focuses on improving efficiency (e.g., through mixed-precision quantization and optimized architectures), enhancing generalization capabilities (particularly to longer sequences), and understanding the underlying mechanisms of in-context learning. These advancements have significant implications for diverse fields, improving the accuracy and efficiency of numerous applications while simultaneously deepening our theoretical understanding of these powerful models.
Papers - Page 46
Transformers generalize differently from information stored in context vs in weights
Stephanie C. Y. Chan, Ishita Dasgupta, Junkyung Kim, Dharshan Kumaran, Andrew K. Lampinen, Felix HillUnderstanding the Failure of Batch Normalization for Transformers in NLP
Jiaxi Wang, Ji Wu, Lei HuangRelational Attention: Generalizing Transformers for Graph-Structured Tasks
Cameron Diao, Ricky Loynd