- Text 1
How do adapters work?
Adapters are small neural network modules that are inserted into pre-trained foundation models like BERT or GPT-4 to adapt them to new tasks or domains. Rather than retraining the entire massive foundation model, only the lightweight adapter modules are trained on the new task data.
Adapters consist of a small feedforward neural network, usually one or two layers, that is injected into the original network. The adapter maps the pre-trained model's hidden state representation to the specialized representation needed for the new task. The adapter and original model are trained together on the downstream task data, allowing the adapter to learn the new mapping while the original model weights remain mostly unchanged.
This approach preserves the knowledge in the original pre-trained weights, saving significant retraining resources. Multiple different adapters can also be added to the same foundation model for multi-task learning. The adapters provide targeted task-specific customization of the model without interfering with each other.
Adapters enable fast task transfer learning for large pre-trained models. By adding lightweight adapters rather than retraining the model end-to-end, they provide an efficient way to adapt these models to new tasks and datasets using limited computational resources. This makes adapters a key technology for effectively leveraging and repurposing AI models in real-world applications.
Why are adapters important?
Adapters make it practical to fully leverage the knowledge in large pre-trained models for real-world applications. They save time, computation, and cost while customizing models for specific tasks.
To add more detail, adapters have several key benefits, including that:
They allow efficient transfer learning from large pre-trained models to new tasks, saving substantial compute resources compared to full fine-tuning.
They enable quick experimentation and iteration on new datasets and use cases using existing foundation model checkpoints.
They allow a single foundation model to be adapted for multiple different tasks, supporting wider reuse of models.
They modularize model components, with adapters focused on specialized domains or objectives.
They reduce the risk of catastrophic forgetting or interference across different adapted tasks.
They enable personalization of shared models to individual users or scenarios.
Why do adapters matter for companies?
Adapters can enable substantial benefits for companies incorporating AI. Rather than paying for full retraining of massive foundation models, adapters reduce compute infrastructure costs by allowing quick task transfer learning.
Companies can speed up experimentation and release of new products and features by building on their existing models using adapters. Any pre-trained models that companies have already invested in can be utilized more extensively through adapters, extracting greater value from them.
Adapters also facilitate efficient customization of AI services for varied customers and markets, tailoring models to specific needs. They even enable personalization of models down to individual users and items. And from a maintenance perspective, adapters modularize knowledge by task, simplifying upkeep as models are adapted.
With adapters, companies can achieve faster AI innovation cycles, expand use cases for their models, maximize return on model investments, drive down costs, and respond rapidly to emerging opportunities. This makes adapters a crucial technology for efficient, scalable AI development for corporations.
