Dynamic Neural Networks Offer Efficient Alternative for Massive NLP Models
A new survey highlights how dynamic neural networks effectively scale natural language processing models without linear increases in computing power. The research explores methods like skimming, mixture of experts, and early exit to handle trillion-parameter models.
A new survey explores dynamic neural networks as a practical solution to the massive computational costs of modern natural language processing. Unlike traditional static models that process every input through the exact same computational path, dynamic neural networks adapt their internal pathways based on the specific data they receive.
This adaptive approach allows network scaling with only sub-linear increases in computation and time. The researchers highlight three primary techniques driving this efficiency: skimming, mixture of experts, and early exit. These methods make it feasible to pretrain language models with trillions of parameters while also enabling faster inference on resource-constrained mobile devices.
Presented at the EACL 2023 Findings, the survey not only summarizes recent progress in these three dynamic architectures but also outlines significant challenges facing the field. The authors point toward necessary directions for future research to fully realize the potential of dynamic computation in natural language processing.