Foundation Model Adapters for Efficient Multi-Task Fine-Tuning
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Solution Overview
Problem
Current methods for adapting a foundation neural network to perform additional computational tasks face issues such as over-fitting, high computational resource requirements, and inefficient storage due to full retraining of all parameters, while techniques like Visual Prompt Tuning suffer from sensitivity to prompt number and increased computational complexity.
Innovation Solution
Apply linear transformations through adapter matrices and bias values to the output of specific layers in the foundation model, training these matrices and biases on a second task while preserving the original model parameters, allowing for efficient adaptation with reduced computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If all parameters of the foundation model are retrained for each second computational task, then the model can adapt to the new task, but the computational resources required increase significantly and the training time extends
Solution Approach 1:
The patent segments the model parameters into two distinct groups: task-independent parameters (shared across all tasks) and task-specific parameters (adapter parameters). This segmentation allows the model to maintain a shared foundation while adapting to different tasks by only modifying the adapter parameters, thereby reducing computational overhead and training time while preserving adaptability.
Solution Approach 2:
The patent implements a universal foundation model that can perform multiple computational tasks by sharing the majority of its parameters across tasks. The task-independent parameters serve all tasks, while task-specific adapters enable adaptation to individual tasks, achieving multi-functionality without retraining the entire model for each task.
2Adaptability or versatility
If all parameters of the foundation model are retrained for each second computational task, then the model achieves good task performance, but the storage space required increases hugely
Solution Approach 1:
The patent segments parameters into shared task-independent parameters stored once, and compact task-specific adapter parameters. This segmentation dramatically reduces storage requirements because the large foundation model parameters are not duplicated for each task, only the small adapter parameters need to be stored separately for each task.
Solution Approach 2:
Instead of copying the entire foundation model parameters for each task, the patent creates lightweight copies only of the necessary adapter parameters. This selective copying approach maintains task-specific adaptability while minimizing storage space consumption.
3Productivity
If only the last layer of the foundation model is retrained using a linear probe, then the computational resources are reduced, but the model performance deteriorates
Solution Approach 1:
The patent moves the adaptation mechanism from the output layer (traditional linear probe approach) to intermediate layers within the model architecture. By inserting adapter modules at strategic intermediate positions, the model can learn task-specific transformations at the feature extraction stage, achieving better performance while maintaining computational efficiency.
Solution Approach 2:
The patent introduces adapter parameters as intermediary components between the shared foundation model and the task output. These adapters act as mediators that transform the general features extracted by the foundation model into task-specific representations, enabling effective adaptation without retraining the entire model.
4Manufacturing precision
If Visual Prompt Tuning is used to improve fine-tuning performance, then accuracy improves, but the computational complexity increases due to self-attention mechanism
Solution Approach 1:
The patent extracts and removes the computationally expensive self-attention mechanism from the adaptation process. Instead of using self-attention to generate prompts, the patent employs simple linear transformations in adapter modules, achieving comparable or better accuracy with significantly reduced computational complexity.
Solution Approach 2:
The patent replaces complex, computationally intensive prompt generation mechanisms with simple, lightweight linear adapter transformations. These cheap linear operations achieve the necessary adaptation without the heavy computational burden of self-attention, making the approach more efficient and scalable.
Data Source
AI summary
A foundation neural network is trained to perform a first computational task. The foundation model has a number of layers, each including a number of functions defined by a set of numerical parameters, and the sets of parameters are trained to teach the foundation neural network the first computational task. Typically, each function receives an input vector (i.e. a plurality of input values), and generates an output vector (i.e. a plurality of output values). The foundation neural network is adapted to form an adapted neural network. In the adapted neural network, for at least one of these functions, a linear transformation is applied to the output (and/or input) values of the function. To learn the second computational task, parameters defining the linear transformation are trained, using a training database of examples of the second computational task, while substantially not changing the numeral parameters defining the functions.