Neural Network Matrix Sharing for Multi-Task Model Adaptation
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Solution Overview
Problem
Existing neural network model adaptation methods, such as LoRA, are inefficient in terms of parameter count and training time, especially when fine-tuning for multiple tasks, as they require separate low-rank matrices for each task without leveraging shared structures across tasks.
Innovation Solution
A framework that supplements pre-trained neural network layers with a shared matrix and task-specific matrices, freezing the pre-trained and shared matrices while updating only the task matrices, reducing the number of parameters and training time by sharing a single low-rank module across tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate low-rank matrices are created for each task without sharing structures, then task-specific performance is improved, but parameter count and training time increase significantly
Solution Approach 1:
The patent merges the low-rank decomposition components by sharing the first matrix (U) across multiple tasks while maintaining separate second matrices (V) for each task. This combining approach reduces the total parameter count from O(k*d*r) to O(d*r + k*r^2) where k is the number of tasks, thereby decreasing training time and computational overhead while preserving task-specific performance through the task-specific V matrices.
Solution Approach 2:
The first matrix (U) serves as a universal component shared across multiple tasks, performing the same function for all tasks. This multi-functionality allows the system to leverage common patterns across tasks while the task-specific second matrices (V) handle task-specific variations, achieving both efficiency and task-specific performance.
2Adaptability or versatility
If separate low-rank matrices are created for each task, then task-specific adaptation is improved, but the number of parameters increases
Solution Approach 1:
The patent merges the low-rank decomposition components by sharing the first matrix (U) across multiple tasks while maintaining separate second matrices (V) for each task. This combining approach reduces the total parameter count from O(k*d*r) to O(d*r + k*r^2) where k is the number of tasks, thereby decreasing training time and computational overhead while preserving task-specific performance through the task-specific V matrices.
Solution Approach 2:
The first matrix (U) serves as a universal component shared across multiple tasks, performing the same function for all tasks. This multi-functionality allows the system to leverage common patterns across tasks while the task-specific second matrices (V) handle task-specific variations, achieving both efficiency and task-specific performance.
3Adaptability or versatility
If pre-trained matrices are supplemented with separate task matrices for each task, then model specialization is improved, but inference complexity increases
Solution Approach 1:
The patent merges the low-rank decomposition components by sharing the first matrix (U) across multiple tasks while maintaining separate second matrices (V) for each task. This combining approach reduces the total parameter count from O(k*d*r) to O(d*r + k*r^2) where k is the number of tasks, thereby decreasing training time and computational overhead while preserving task-specific performance through the task-specific V matrices.
Solution Approach 2:
The patent segments the adaptation parameters into two distinct components: a shared first matrix (U) that captures common patterns across tasks, and task-specific second matrices (V) that capture task-specific variations. This segmentation allows for efficient storage and retrieval during inference, as the shared component is computed once and reused, while only the smaller task-specific components need to be maintained separately.
Data Source
AI summary
Some aspects relate to technologies for neural network model adaptation and inference for multiple tasks via matrix sharing. In accordance with some aspects, a neural network model is accessed that has a pre-trained matrix at a layer of the neural network model. A shared matrix and a task matrix are added to the pre-trained matrix at the layer of the neural network model. The neural network model is trained for a plurality of tasks by updating the task matrix for each task to provide a trained task matrix for each task while maintaining the pre-trained matrix and the shared matrix the same for all tasks.